In [328]:
# coding = utf-8
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy as sc
import pdb
In [329]:
warnings.filterwarnings('ignore') # "error", "ignore", "always", "default", "module", "once"
In [330]:
# 定义源数据文件名
title = ["debt", "dev", "risk", "stock", "op", "manage", "audit", "violate", "cash", "nps", "net", "govern", "governother"]
title_chinese = ["偿债能力", "发展能力", "风险水平", "股本结构", "经营能力", "内控是否有效", "审计意见", "违规处理公司", "现金流量",
"现金流量---每股现金净流量和每股经营", "盈利能力", "治理结构--会议情况", "治理结构其余数据"]
title_check = list(zip(title, title_chinese))
# 用字典定义文件目录路径
filespath = {title[i] : "Raw Data/" + title_chinese[i] + "/" for i in range(len(title))}
filespath
Out[330]:
{'audit': 'Raw Data/审计意见/',
'cash': 'Raw Data/现金流量/',
'debt': 'Raw Data/偿债能力/',
'dev': 'Raw Data/发展能力/',
'govern': 'Raw Data/治理结构--会议情况/',
'governother': 'Raw Data/治理结构其余数据/',
'manage': 'Raw Data/内控是否有效/',
'net': 'Raw Data/盈利能力/',
'nps': 'Raw Data/现金流量---每股现金净流量和每股经营/',
'op': 'Raw Data/经营能力/',
'risk': 'Raw Data/风险水平/',
'stock': 'Raw Data/股本结构/',
'violate': 'Raw Data/违规处理公司/'}
In [331]:
# 用字典定义各文件目录下的数据文件
filesset = {}
filesset[title[0]] = ["FI_T1", "FI_T11", "FI_T12", "FI_T13"]
filesset[title[1]] = ["FI_T8", "FI_T81", "FI_T82", "FI_T83"]
filesset[title[2]] = ["FI_T7", "FI_T71", "FI_T72", "FI_T73"]
filesset[title[3]] = ["HLD_Capstru"]
filesset[title[4]] = ["FI_T4", "FI_T41","FI_T42", "FI_T43"]
filesset[title[5]] = ["IC_EvaluationRepInfo"]
filesset[title[6]] = ["FIN_Audit"]
filesset[title[7]] = ["STK_Violation_Main"]
filesset[title[8]] = ["FI_T6", "FI_T61", "FI_T62", "FI_T63"]
filesset[title[9]] = ["FI_T9", "FI_T91", "FI_T92", "FI_T93"]
filesset[title[10]] = ["FI_T5", "FI_T51", "FI_T52", "FI_T53"]
filesset[title[11]] = ["CG_Agm"]
filesset[title[12]] = ["CG_Ybasic"]
# 生成各财务主题目录下的文件路径
filespath = {key : [filespath[key] + filesset[key][i] + ".csv" for i in range(len(filesset[key]))] for key in filespath}
filespath
Out[331]:
{'audit': ['Raw Data/审计意见/FIN_Audit.csv'],
'cash': ['Raw Data/现金流量/FI_T6.csv',
'Raw Data/现金流量/FI_T61.csv',
'Raw Data/现金流量/FI_T62.csv',
'Raw Data/现金流量/FI_T63.csv'],
'debt': ['Raw Data/偿债能力/FI_T1.csv',
'Raw Data/偿债能力/FI_T11.csv',
'Raw Data/偿债能力/FI_T12.csv',
'Raw Data/偿债能力/FI_T13.csv'],
'dev': ['Raw Data/发展能力/FI_T8.csv',
'Raw Data/发展能力/FI_T81.csv',
'Raw Data/发展能力/FI_T82.csv',
'Raw Data/发展能力/FI_T83.csv'],
'govern': ['Raw Data/治理结构--会议情况/CG_Agm.csv'],
'governother': ['Raw Data/治理结构其余数据/CG_Ybasic.csv'],
'manage': ['Raw Data/内控是否有效/IC_EvaluationRepInfo.csv'],
'net': ['Raw Data/盈利能力/FI_T5.csv',
'Raw Data/盈利能力/FI_T51.csv',
'Raw Data/盈利能力/FI_T52.csv',
'Raw Data/盈利能力/FI_T53.csv'],
'nps': ['Raw Data/现金流量---每股现金净流量和每股经营/FI_T9.csv',
'Raw Data/现金流量---每股现金净流量和每股经营/FI_T91.csv',
'Raw Data/现金流量---每股现金净流量和每股经营/FI_T92.csv',
'Raw Data/现金流量---每股现金净流量和每股经营/FI_T93.csv'],
'op': ['Raw Data/经营能力/FI_T4.csv',
'Raw Data/经营能力/FI_T41.csv',
'Raw Data/经营能力/FI_T42.csv',
'Raw Data/经营能力/FI_T43.csv'],
'risk': ['Raw Data/风险水平/FI_T7.csv',
'Raw Data/风险水平/FI_T71.csv',
'Raw Data/风险水平/FI_T72.csv',
'Raw Data/风险水平/FI_T73.csv'],
'stock': ['Raw Data/股本结构/HLD_Capstru.csv'],
'violate': ['Raw Data/违规处理公司/STK_Violation_Main.csv']}
In [332]:
# 用字典推导式生成字典结构的TextFileReader iterable object。
files_reader = {key : pd.concat(pd.read_csv(filepath, iterator=True, chunksize=10000, delimiter=","), ignore_index=True) for key in filespath
for filepath in filespath[key]}
# 报表类型编码A表示合并报表，B表示母公司报表。一般财务考察合并报表。
files_reader["debt"].head()
Out[332]:
| Stkcd | Accper | Typrep | Indcd | F010101A | F010201A | F010301A | F010401A | F010702B | F011201A | F011601A | F011701A | F011801A | F012601B | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 股票代码 | 截止日期 | 报表类型编码 | 行业代码 | 流动比率 | 速动比率 | 保守速动比率 | 现金比率 | 利息保障倍数B | 资产负债率 | 权益乘数 | 产权比率 | 权益对负债比率 | 有形净值债务率 |
| 1 | 没有单位 | 没有单位 | 没有单位 | 没有单位 | 没有单位 | 没有单位 | 没有单位 | 没有单位 | 没有单位 | 没有单位 | 没有单位 | 没有单位 | 没有单位 | 没有单位 |
| 2 | 600675 | 2005/6/30 | A | K70 | 1.389744 | 0.371053 | 0.294101 | NaN | 4.612133 | 0.697352 | 3.304172 | 2.304172 | 0.433995 | 2.325622 |
| 3 | 600675 | 2005/6/30 | B | K70 | 0.789221 | 0.689505 | 0.623871 | 0.122484 | 6.461849 | 0.540862 | 2.177996 | 1.177996 | 0.848899 | NaN |
| 4 | 600675 | 2005/9/30 | A | K70 | 1.572825 | 0.575876 | 0.4889 | NaN | 6.23885 | 0.674823 | 3.075247 | 2.075247 | 0.48187 | 2.092604 |
In [333]:
# 对存在报表类型编码的dataframe分别筛选出A和B类型。A为合并报表，是财务上主要考察的报表。B报表是母公司报表，相对次要。
areport = {key : files_reader[key][(files_reader[key]["Typrep"] == "A")]
if "Typrep" in files_reader[key].columns else files_reader[key] for key in title}
breport = {key : files_reader[key][(files_reader[key].Typrep == "B")]
if "Typrep" in files_reader[key].columns else files_reader[key] for key in title}
# title = ["debt", "dev", "risk", "stock", "op", "manage", "audit", "violate", "cash", "nps", "net", "govern", "governother"]
areport["debt"].head()
Out[333]:
| Stkcd | Accper | Typrep | Indcd | F010101A | F010201A | F010301A | F010401A | F010702B | F011201A | F011601A | F011701A | F011801A | F012601B | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 600675 | 2005/6/30 | A | K70 | 1.389744 | 0.371053 | 0.294101 | NaN | 4.612133 | 0.697352 | 3.304172 | 2.304172 | 0.433995 | 2.325622 |
| 4 | 600675 | 2005/9/30 | A | K70 | 1.572825 | 0.575876 | 0.4889 | NaN | 6.23885 | 0.674823 | 3.075247 | 2.075247 | 0.48187 | 2.092604 |
| 6 | 600675 | 2005/12/31 | A | K70 | 1.448779 | 0.451555 | 0.434195 | NaN | 15.877637 | 0.666081 | 2.994737 | 1.994737 | 0.501319 | 2.01045 |
| 9 | 600675 | 2006/3/31 | A | K70 | 1.464401 | 0.505022 | 0.489321 | NaN | 4.838706 | 0.689245 | 3.217973 | 2.217973 | 0.450862 | 2.235172 |
| 10 | 600675 | 2006/6/30 | A | K70 | 1.384693 | 0.553543 | 0.5368 | NaN | 6.631475 | 0.681799 | 3.142667 | 2.142667 | 0.466708 | 2.159064 |
In [334]:
# 修改aresult和bresult的columns name。
for key in title:
areport[key].columns = files_reader[key].loc[0]
breport[key].columns = files_reader[key].loc[0]
# 对出现缺失值的列名，进行填充。
areport["violate"].columns = areport["violate"].columns.fillna("违规年度")
breport["violate"].columns = breport["violate"].columns.fillna("违规年度")
for key in title:
print("aresult[%s]的columns name is\n"%key, areport[key].columns)
print("bresult[%s]的columns name is\n"%key, areport[key].columns)
aresult[debt]的columns name is
Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '流动比率', '速动比率', '保守速动比率', '现金比率',
'利息保障倍数B', '资产负债率', '权益乘数', '产权比率', '权益对负债比率', '有形净值债务率'],
dtype='object', name=0)
bresult[debt]的columns name is
Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '流动比率', '速动比率', '保守速动比率', '现金比率',
'利息保障倍数B', '资产负债率', '权益乘数', '产权比率', '权益对负债比率', '有形净值债务率'],
dtype='object', name=0)
aresult[dev]的columns name is
Index(['股票代码', '会计年度', '报表类型编码', '行业代码', '固定资产增长率B', '总资产增长率B', '净资产收益率增长率B',
'净利润增长率B', '利润总额增长率B', '营业利润增长率B', '营业收入增长率B', '所有者权益增长率B'],
dtype='object', name=0)
bresult[dev]的columns name is
Index(['股票代码', '会计年度', '报表类型编码', '行业代码', '固定资产增长率B', '总资产增长率B', '净资产收益率增长率B',
'净利润增长率B', '利润总额增长率B', '营业利润增长率B', '营业收入增长率B', '所有者权益增长率B'],
dtype='object', name=0)
aresult[risk]的columns name is
Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '财务杠杆', '经营杠杆', '综合杠杆'], dtype='object', name=0)
bresult[risk]的columns name is
Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '财务杠杆', '经营杠杆', '综合杠杆'], dtype='object', name=0)
aresult[stock]的columns name is
Index(['证券代码', '统计截止日期', '股本总数', '其中：国有股', '其中：一般法人配售', '已流通股份'], dtype='object', name=0)
bresult[stock]的columns name is
Index(['证券代码', '统计截止日期', '股本总数', '其中：国有股', '其中：一般法人配售', '已流通股份'], dtype='object', name=0)
aresult[op]的columns name is
Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '应收账款与收入比', '应收账款周转率B', '存货与收入比',
'存货周转率B', '应付账款周转率B', '营运资金（资本）周转率B', '现金及现金等价物周转率B', '流动资产与收入比',
'流动资产周转率B', '固定资产与收入比', '固定资产周转率B', '非流动资产周转率B', '总资产周转率B', '股东权益周转率B'],
dtype='object', name=0)
bresult[op]的columns name is
Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '应收账款与收入比', '应收账款周转率B', '存货与收入比',
'存货周转率B', '应付账款周转率B', '营运资金（资本）周转率B', '现金及现金等价物周转率B', '流动资产与收入比',
'流动资产周转率B', '固定资产与收入比', '固定资产周转率B', '非流动资产周转率B', '总资产周转率B', '股东权益周转率B'],
dtype='object', name=0)
aresult[manage]的columns name is
Index(['股票代码', '统计截止日期', '所属省份', '行业代码', '行业名称', '是否披露内控评价报告', '是否出具内控评价报告结论',
'内部控制是否有效', '内部控制是否存在缺陷', '是否采取整改措施', '评价结论内容'],
dtype='object', name=0)
bresult[manage]的columns name is
Index(['股票代码', '统计截止日期', '所属省份', '行业代码', '行业名称', '是否披露内控评价报告', '是否出具内控评价报告结论',
'内部控制是否有效', '内部控制是否存在缺陷', '是否采取整改措施', '评价结论内容'],
dtype='object', name=0)
aresult[audit]的columns name is
Index(['证券代码', '证券简称', '会计截止日期', '审计日期', '审计意见类型', '审计师', '境内审计事务所', '境外审计事务所',
'境内审计费用金额', '境内审计费用货币单位', '境外审计费用金额', '境外审计费用货币单位', '其他相关费用金额',
'其他相关费用货币单位', '审计费用合计', '合计审计费用货币单位', '审计报告', '审计费用说明'],
dtype='object', name=0)
bresult[audit]的columns name is
Index(['证券代码', '证券简称', '会计截止日期', '审计日期', '审计意见类型', '审计师', '境内审计事务所', '境外审计事务所',
'境内审计费用金额', '境内审计费用货币单位', '境外审计费用金额', '境外审计费用货币单位', '其他相关费用金额',
'其他相关费用货币单位', '审计费用合计', '合计审计费用货币单位', '审计报告', '审计费用说明'],
dtype='object', name=0)
aresult[violate]的columns name is
Index(['违规事件ID', '证券代码', '处理文件日期', '公告日期', '公告发布机构', '公告文件名称', '处理文件编号',
'处理单位', '违规类型', '违规类型编码', '违反的法律法规', '违规年度', '违规年度', '违规年度', '违规年度',
'违规年度', '违规年度', '违规年度', '违规年度', '违规年度', '违规年度', '违规年度', '违规年度', '违规年度',
'违规年度', '违规年度', '违规年度', '违规行为', '处分措施', '处罚总金额', '上市公司是否违规',
'处罚方式-上市公司', '处罚方式编码-上市公司', '处罚金额-上市公司'],
dtype='object', name=0)
bresult[violate]的columns name is
Index(['违规事件ID', '证券代码', '处理文件日期', '公告日期', '公告发布机构', '公告文件名称', '处理文件编号',
'处理单位', '违规类型', '违规类型编码', '违反的法律法规', '违规年度', '违规年度', '违规年度', '违规年度',
'违规年度', '违规年度', '违规年度', '违规年度', '违规年度', '违规年度', '违规年度', '违规年度', '违规年度',
'违规年度', '违规年度', '违规年度', '违规行为', '处分措施', '处罚总金额', '上市公司是否违规',
'处罚方式-上市公司', '处罚方式编码-上市公司', '处罚金额-上市公司'],
dtype='object', name=0)
aresult[cash]的columns name is
Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '营业收入现金净含量', '营业利润现金净含量', '营运指数'], dtype='object', name=0)
bresult[cash]的columns name is
Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '营业收入现金净含量', '营业利润现金净含量', '营运指数'], dtype='object', name=0)
aresult[nps]的columns name is
Index(['每股现金净流量TTM2', '每股经营活动产生的现金流量净额TTM2'], dtype='object', name=0)
bresult[nps]的columns name is
Index(['每股现金净流量TTM2', '每股经营活动产生的现金流量净额TTM2'], dtype='object', name=0)
aresult[net]的columns name is
Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '资产报酬率B', '总资产净利润率（ROA）B', '净资产收益率B',
'营业利润率'],
dtype='object', name=0)
bresult[net]的columns name is
Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '资产报酬率B', '总资产净利润率（ROA）B', '净资产收益率B',
'营业利润率'],
dtype='object', name=0)
aresult[govern]的columns name is
Index(['证券代码', '统计截止日期', '董事会会议次数', '监事会会议次数', '备注', '股东大会召开次数'], dtype='object', name=0)
bresult[govern]的columns name is
Index(['证券代码', '统计截止日期', '董事会会议次数', '监事会会议次数', '备注', '股东大会召开次数'], dtype='object', name=0)
aresult[governother]的columns name is
Index(['证券代码', '统计截止日期', '股本结构是否变化', '董事长与总经理兼任情况', '董事人数', '其中：独立董事人数',
'监事总规模'],
dtype='object', name=0)
bresult[governother]的columns name is
Index(['证券代码', '统计截止日期', '股本结构是否变化', '董事长与总经理兼任情况', '董事人数', '其中：独立董事人数',
'监事总规模'],
dtype='object', name=0)
In [335]:
# 以运营科目的财报内容为例展示源数据内容
areport["op"].head()
Out[335]:
| 股票代码 | 截止日期 | 报表类型编码 | 行业代码 | 应收账款与收入比 | 应收账款周转率B | 存货与收入比 | 存货周转率B | 应付账款周转率B | 营运资金（资本）周转率B | 现金及现金等价物周转率B | 流动资产与收入比 | 流动资产周转率B | 固定资产与收入比 | 固定资产周转率B | 非流动资产周转率B | 总资产周转率B | 股东权益周转率B | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 600717 | 2007/6/30 | A | G59 | 0.114394 | 10.043208 | 0.01013 | 59.662912 | 14.993807 | -4.007609 | 1.16887 | 1.161457 | 0.826862 | 3.327115 | 0.297336 | 0.209558 | 0.167186 | 0.248113 |
| 5 | 600717 | 2007/9/30 | A | G59 | 0.069617 | 16.312664 | 0.019036 | 45.55388 | 21.829519 | 16.700731 | 1.123689 | 1.509898 | 0.871064 | 2.095157 | 0.47387 | 0.317013 | 0.232425 | 0.367831 |
| 6 | 600717 | 2007/12/31 | A | G59 | 0.044992 | 24.225777 | 0.011198 | 76.852855 | 35.382099 | 46.271628 | 2.4801 | 1.082133 | 1.219918 | 1.487035 | 0.668048 | 0.420121 | 0.312501 | 0.484882 |
| 9 | 600717 | 2008/3/31 | A | G59 | 0.22405 | 5.175955 | 0.040047 | 14.891194 | 2.068903 | 1.166469 | 0.754384 | 3.64115 | 0.265042 | 8.200662 | 0.147421 | 0.092819 | 0.068744 | 0.111194 |
| 10 | 600717 | 2008/6/30 | A | G59 | 0.312123 | 3.624538 | 0.063661 | 13.932489 | 2.477918 | -86.44711 | 3.074194 | 0.815366 | 1.118961 | 1.536829 | 0.766725 | 0.438084 | 0.314826 | 0.544186 |
In [336]:
# 准备好了源数据。下面开始对源数据清洗。先检查并删除重复行。
# 观察所有样本集，发现审计样本集的字段名称与其它样本集不同。所以先将审计样本集的“证券简称”列删除。方便后续步骤。
try:
areport['audit'].drop(labels=["证券简称"], axis=1, inplace=True)
except KeyError:
pass
areport_drop_duplicates = {key : areport[key].drop_duplicates([areport[key].columns[0], areport[key].columns[1]])
for key in areport}
breport_drop_duplicates = {key : breport[key].drop_duplicates([breport[key].columns[0], areport[key].columns[1]])
for key in breport}
# 展示偿债能力相关的财报内容
areport_drop_duplicates["audit"].shape
Out[336]:
(38906, 17)
In [337]:
# 后面只使用合并报表，即报表类型A的数据。考虑到尽量使用全部财务指标，故删除areport的所有空行记录。
areport_dropna = {key : areport_drop_duplicates[key].dropna() for key in title}
# 查看偿债能力相关内容的财报的数据类型。
areport_drop_duplicates["debt"].dtypes
Out[337]:
0 股票代码 object 截止日期 object 报表类型编码 object 行业代码 object 流动比率 object 速动比率 object 保守速动比率 object 现金比率 object 利息保障倍数B object 资产负债率 object 权益乘数 object 产权比率 object 权益对负债比率 object 有形净值债务率 object dtype: object
In [338]:
# 偿债能力相关财报的字段名：股票代码，截止日期，报表类型编码，行业代码，流动比率，速动比率，保守速动比率，现金比率，利息保障倍数B，
# 资产负债率，权益乘数，产权比率，权益对负债比率，有形净值债务率
# 删除重复样本的数据集名：areport_drop_duplicates
# title = ["debt", "dev", "risk", "stock", "op", "manage", "audit", "violate", "cash", "nps", "net", "govern", "governother"]
areport_drop_duplicates["debt"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["debt"]["截止日期"])
areport_astype_debt = areport_drop_duplicates["debt"].astype({"股票代码":"int64", "报表类型编码":"category",
"行业代码":"category", "流动比率":"float64", "速动比率":"float64",
"保守速动比率":"float64", "现金比率":"float64", "利息保障倍数B":"float64",
"资产负债率":"float64", "权益乘数":"float64", "产权比率":"float64",
"权益对负债比率":"float64", "有形净值债务率":"float64"})
# 继续对其它财务主题的源数据报表做预处理。预处理包括：统一字段名称，修改字段数据类型。
# 先将与其它表不一致的固定列名修改为一致。即将会计年度改为截止日期。
# 发展能力相关财报源数据的字段名：股票代码, 截止日期, 报表类型编码, 行业代码, 固定资产增长率B, 总资产增长率B, 净资产收益率增长率B,
# 净利润增长率B,
# 利润总额增长率B, 营业利润增长率B, 营业收入增长率B, 所有者权益增长率B
areport_drop_duplicates["dev"].rename(columns = {"会计年度":"截止日期"}, inplace=True)
# 将截止日期列的数据类型改为detetime64[ns]
areport_drop_duplicates["dev"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["dev"]["截止日期"])
areport_astype_dev = areport_drop_duplicates["dev"].astype({"股票代码":"int64", "报表类型编码":"category", "行业代码":"category",
"固定资产增长率B":"float64", "总资产增长率B":"float64",
"净资产收益率增长率B":"float64","净利润增长率B":"float64",
"利润总额增长率B":"float64", "营业利润增长率B":"float64",
"营业收入增长率B":"float64", "所有者权益增长率B":"float64",})
# 风险相关的财报字段名：股票代码, 截止日期, 报表类型编码, 行业代码, 财务杠杆, 经营杠杆, 综合杠杆。
areport_drop_duplicates["risk"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["risk"]["截止日期"])
areport_astype_risk = areport_drop_duplicates["risk"].astype({"股票代码":"int64", "报表类型编码":"category", "行业代码":"category",
"财务杠杆":"float64", "经营杠杆":"float64", "综合杠杆":"float64"})
# 股权主题相关的财报字段：证券代码, 统计截止日期, 股本总数, 其中：国有股, 其中：一般法人配售, 已流通股份
# 修改列名
areport_drop_duplicates["stock"].rename(columns={"证券代码":"股票代码", "统计截止日期":"截止日期", "其中：国有股":"国有股",
"其中：一般法人配售":"一般法人配售"}, inplace=True)
# 删除错误的第1行
try:
areport_drop_duplicates["stock"].drop([0,1], inplace=True)
except KeyError:
pass
# 修改数据类型
areport_drop_duplicates["stock"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["stock"]["截止日期"])
areport_astype_stock = areport_drop_duplicates["stock"].astype({"股票代码":"int64", "股本总数":"int64", "国有股":"int64",
"一般法人配售":"int64", "已流通股份":"int64"})
# 运营主题相关的财报字段：股票代码, 截止日期, 报表类型编码, 行业代码, 应收账款与收入比, 应收账款周转率B, 存货与收入比, 存货周转率B,
# 应付账款周转率B, 营运资金（资本）周转率B, 现金及现金等价物周转率B, 流动资产与收入比, 流动资产周转率B, 固定资产与收入比,
# 固定资产周转率B, 非流动资产周转率B, 总资产周转率B, 股东权益周转率B
# 修改字段数据类型
areport_drop_duplicates["op"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["op"]["截止日期"])
areport_astype_op = areport_drop_duplicates["op"].astype({"股票代码":"int64", "报表类型编码":"category", "行业代码":"category",
"应收账款与收入比":"float64", "应收账款周转率B":"float64",
"存货与收入比":"float64", "存货周转率B":"float64", "应付账款周转率B":"float64",
"营运资金（资本）周转率B":"float64", "现金及现金等价物周转率B":"float64",
"流动资产与收入比":"float64", "流动资产周转率B":"float64",
"固定资产与收入比":"float64", "固定资产周转率B":"float64",
"非流动资产周转率B":"float64", "总资产周转率B":"float64",
"股东权益周转率B":"float64"})
# 管控主题相关的财报字段：股票代码, 截止日期, 所属省份, 行业代码, 行业名称, 是否披露内控评价报告, 是否出具内控评价报告结论,
# 内部控制是否有效, 内部控制是否存在缺陷, 是否采取整改措施, 评价结论内容
try:
areport_drop_duplicates["manage"].drop([0,1], inplace=True)
except KeyError:
pass
# 修改固定列名与其它数据集一致。
areport_drop_duplicates["manage"].rename(columns={"统计截止日期":"截止日期"}, inplace=True)
areport_drop_duplicates["manage"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["manage"]["截止日期"])
areport_astype_manage = areport_drop_duplicates["manage"].astype({"股票代码":"int64", "行业代码":"category",
"是否披露内控评价报告":"category",
"是否出具内控评价报告结论":"category", "内部控制是否有效":"category",
"内部控制是否存在缺陷":"category", "是否采取整改措施":"category",
})
# 对管控财报的字段观察后发现，"是否披露内控评价报告", "是否出具内控评价报告结论", "内部控制是否有效", "内部控制是否存在缺陷",
# "是否采取整改措施" 这几个字段对分析没有用处，故删除这些字段。
try:
areport_astype_manage.drop(labels=["是否披露内控评价报告", "是否出具内控评价报告结论", "内部控制是否有效", "内部控制是否存在缺陷",
"是否采取整改措施", "评价结论内容", "行业代码"], axis=1, inplace=True)
except KeyError:
pass
# 审计相关的财报字段：股票代码, 证券简称, 截止日期, 审计日期, 审计意见类型, 审计师, 境内审计事务所, 境外审计事务所, 境内审计费用金额,
# 境内审计费用货币单位, 境外审计费用金额, 境外审计费用货币单位, 其他相关费用金额, 其他相关费用货币单位, 审计费用合计,
# 合计审计费用货币单位, 审计报告, 审计费用说明
# 修改列名。
areport_drop_duplicates["audit"].rename(columns={"证券代码":"股票代码", "会计截止日期":"截止日期"}, inplace=True)
# 删除第0，1行。
try:
areport_drop_duplicates["audit"].drop([0,1], inplace=True)
except KeyError:
pass
# 修改数据类型
areport_drop_duplicates["audit"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["audit"]["截止日期"])
areport_drop_duplicates["audit"]["审计日期"] = pd.to_datetime(areport_drop_duplicates["audit"]["审计日期"], errors = "coerce")
areport_astype_audit = areport_drop_duplicates["audit"].astype({"股票代码":"int64", "境内审计费用金额":"float64",
"境内审计费用货币单位":"category", "境外审计费用金额":"float64",
"境外审计费用货币单位":"category", "其他相关费用金额":"float64",
"其他相关费用货币单位":"category", "审计费用合计":"float64", "合计审计费用货币单位":"category", })
# 对审计源数据观察后发现，我们只需要使用“股票代码”，“截止日期”和“审计意见类型”三个字段，其它字段无用，故删除。
try:
areport_astype_audit.drop(labels=["审计日期", "审计师", "境内审计事务所", "境外审计事务所", "境内审计费用金额",
"境内审计费用货币单位", "境外审计费用金额", "境外审计费用货币单位", "其他相关费用金额", "其他相关费用货币单位", "审计费用合计",
"合计审计费用货币单位", "审计报告", "审计费用说明"], axis=1, inplace=True)
except KeyError:
pass
# 违规相关的财报字段：违规事件ID, 股票代码, 处理文件日期, 公告日期, 公告发布机构, 公告文件名称, 处理文件编号, 处理单位, 违规类型,
# 违规类型编码, 违反的法律法规, 违规年度, 违规行为, 处分措施, 处罚总金额, 上市公司是否违规, 处罚方式-上市公司, 处罚方式编码-上市公司,
# 处罚金额-上市公司
# 修改数据集的属性名称
areport_drop_duplicates["violate"].rename(columns={"证券代码":"股票代码"}, inplace=True)
# 删除数据集中的第0，1行。
try:
areport_drop_duplicates["violate"].drop([0,1], inplace=True)
except KeyError:
pass
# 这个数据集的清洗比较复杂，因为各属性有大量缺失值，需要一一填充，以方便数据类型的转换。注意，NaN的类型为float，所以如果要将NaN转换为int，
# 需要先把该列fillna(int)，再astype。
areport_drop_duplicates["violate"]["违规年度"] = areport_drop_duplicates["violate"]["违规年度"].fillna(0)
areport_drop_duplicates["violate"]["处罚方式-上市公司"] = areport_drop_duplicates["violate"]["处罚方式-上市公司"].fillna("无")
areport_drop_duplicates["violate"]["处理文件日期"] = pd.to_datetime(areport_drop_duplicates["violate"]["处理文件日期"])
areport_drop_duplicates["violate"]["公告日期"] = pd.to_datetime(areport_drop_duplicates["violate"]["公告日期"], errors="coerce")
areport_drop_duplicates["violate"]
areport_astype_violate = areport_drop_duplicates["violate"].astype({"违规事件ID":"int64", "股票代码":"int64", "公告发布机构":"category",
"处理单位":"category", "违规类型":"category", "违规类型编码":"category"
, "违规年度":"int32", "处罚总金额":"float64",
"上市公司是否违规":"category", "处罚方式-上市公司":"category",
"处罚金额-上市公司":"float64" })
# 现金流相关的财报字段：股票代码, 截止日期, 报表类型编码, 行业代码, 营业收入现金净含量, 营业利润现金净含量, 营运指数
# 修改数据类型
areport_drop_duplicates["cash"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["cash"]["截止日期"], format="%Y-%m-%d")
areport_astype_cash = areport_drop_duplicates["cash"].astype({"股票代码":"int64", "报表类型编码":"category", "行业代码":"category",
"营业收入现金净含量":"float64", "营业利润现金净含量":"float64",
"营运指数":"float64"})
# 净利润相关的财报字段：股票代码, 截止日期, 报表类型编码, 行业代码, 资产报酬率B, 总资产净利润率（ROA）B, 净资产收益率B, 营业利润率
# 修改数据类型
areport_drop_duplicates["net"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["net"]["截止日期"])
areport_astype_net = areport_drop_duplicates["net"].astype({"股票代码":"int64", "报表类型编码":"category", "行业代码":"category",
"资产报酬率B":"float64", "总资产净利润率（ROA）B":"float64",
"净资产收益率B":"float64", "营业利润率":"float64"})
# 治理结构相关字段：股票代码, 截止日期, 董事会会议次数, 监事会会议次数, 备注, 股东大会召开次数
# 修改数据类型
areport_drop_duplicates["govern"].rename(columns={"证券代码":"股票代码", "统计截止日期":"截止日期"}, inplace=True)
try:
areport_drop_duplicates["govern"] = areport_drop_duplicates["govern"].drop([0,1]) # 删除第0，1行
areport_drop_duplicates["govern"].drop(["备注"], axis=1, inplace=True) # 删除备注列。
except KeyError:
pass
areport_drop_duplicates["govern"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["govern"]["截止日期"])
# 对缺失次数填充0
areport_drop_duplicates["govern"]["董事会会议次数"].fillna(0, inplace=True)
areport_drop_duplicates["govern"]["监事会会议次数"].fillna(0, inplace=True)
areport_drop_duplicates["govern"]["股东大会召开次数"].fillna(0, inplace=True)
areport_astype_govern = areport_drop_duplicates["govern"].astype({"股票代码":"int64", "董事会会议次数":"int32",
"监事会会议次数":"int32", "股东大会召开次数":"int32" })
# 治理结构（其余）相关的字段：股票代码, 截止日期, 股本结构是否变化, 董事长与总经理兼任情况, 董事人数, 其中：独立董事人数, 监事总规模
# 修改数据集的列名
areport_drop_duplicates["governother"].rename(columns={"证券代码":"股票代码", "统计截止日期":"截止日期"}, inplace=True)
# 删除第0，1行
try:
areport_drop_duplicates["governother"].drop([0,1], inplace=True)
except KeyError:
pass
# 填充缺失值为0
areport_drop_duplicates["governother"].fillna(0, inplace=True)
# 修改数据集的数据类型
areport_drop_duplicates["governother"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["governother"]["截止日期"])
areport_astype_governother = areport_drop_duplicates["governother"].astype({"股票代码":"int64", "股本结构是否变化":"category",
"董事长与总经理兼任情况":"category", "董事人数":"int32",
"其中：独立董事人数":"int32", "监事总规模":"int32" })
# 观察字段，发现'股本结构是否变化', '董事长与总经理兼任情况'两个字段可以删除。
try:
areport_astype_governother.drop(labels=['股本结构是否变化', '董事长与总经理兼任情况'], axis=1, inplace=True)
except KeyError:
pass
In [339]:
areport_astype_audit
Out[339]:
| 股票代码 | 截止日期 | 审计意见类型 | |
|---|---|---|---|
| 2 | 1 | 2000-12-31 | 标准无保留意见 |
| 3 | 1 | 2001-06-30 | 标准无保留意见 |
| 4 | 1 | 2001-12-31 | 标准无保留意见 |
| 5 | 1 | 2002-06-30 | 标准无保留意见 |
| 6 | 1 | 2002-12-31 | 标准无保留意见 |
| 7 | 1 | 2003-06-30 | 标准无保留意见 |
| 8 | 1 | 2003-12-31 | 标准无保留意见 |
| 9 | 1 | 2004-06-30 | 标准无保留意见 |
| 10 | 1 | 2004-12-31 | 无保留意见加事项段 |
| 11 | 1 | 1992-12-31 | 标准无保留意见 |
| 12 | 1 | 1993-12-31 | 标准无保留意见 |
| 13 | 1 | 1994-12-31 | 标准无保留意见 |
| 14 | 1 | 1995-12-31 | 标准无保留意见 |
| 15 | 1 | 1996-06-30 | 标准无保留意见 |
| 16 | 1 | 1996-12-31 | 标准无保留意见 |
| 17 | 1 | 1997-06-30 | 标准无保留意见 |
| 18 | 1 | 1997-12-31 | 标准无保留意见 |
| 19 | 1 | 1998-06-30 | 标准无保留意见 |
| 20 | 1 | 1998-12-31 | 标准无保留意见 |
| 21 | 1 | 1999-12-31 | 标准无保留意见 |
| 22 | 1 | 2011-12-31 | 标准无保留意见 |
| 23 | 1 | 2012-06-30 | 标准无保留意见 |
| 24 | 1 | 2000-06-30 | 标准无保留意见 |
| 25 | 1 | 2005-06-30 | 标准无保留意见 |
| 26 | 1 | 2005-12-31 | 标准无保留意见 |
| 27 | 1 | 2006-06-30 | 标准无保留意见 |
| 28 | 1 | 2006-12-31 | 标准无保留意见 |
| 29 | 1 | 2007-06-30 | 标准无保留意见 |
| 30 | 1 | 2007-12-31 | 标准无保留意见 |
| 31 | 1 | 2008-06-30 | 标准无保留意见 |
| ... | ... | ... | ... |
| 38937 | 900956 | 2005-12-31 | 标准无保留意见 |
| 38938 | 900956 | 2006-12-31 | 标准无保留意见 |
| 38939 | 900956 | 2007-12-31 | 标准无保留意见 |
| 38940 | 900956 | 2008-12-31 | 标准无保留意见 |
| 38941 | 900956 | 2009-12-31 | 标准无保留意见 |
| 38942 | 900956 | 2010-12-31 | 标准无保留意见 |
| 38943 | 900956 | 2000-12-31 | 标准无保留意见 |
| 38944 | 900956 | 2011-12-31 | 标准无保留意见 |
| 38945 | 900956 | 2012-12-31 | 标准无保留意见 |
| 38946 | 900956 | 2013-12-31 | 标准无保留意见 |
| 38947 | 900956 | 2014-12-31 | 标准无保留意见 |
| 38948 | 900956 | 2015-12-31 | 标准无保留意见 |
| 38949 | 900956 | 2016-12-31 | 标准无保留意见 |
| 38950 | 900957 | 2000-12-31 | 标准无保留意见 |
| 38951 | 900957 | 2001-12-31 | 标准无保留意见 |
| 38952 | 900957 | 2011-12-31 | 标准无保留意见 |
| 38953 | 900957 | 2002-12-31 | 保留意见 |
| 38954 | 900957 | 2003-12-31 | 标准无保留意见 |
| 38955 | 900957 | 2004-12-31 | 标准无保留意见 |
| 38956 | 900957 | 2005-12-31 | 无保留意见加事项段 |
| 38957 | 900957 | 2006-12-31 | 标准无保留意见 |
| 38958 | 900957 | 2007-12-31 | 标准无保留意见 |
| 38959 | 900957 | 2008-12-31 | 标准无保留意见 |
| 38960 | 900957 | 2009-12-31 | 标准无保留意见 |
| 38961 | 900957 | 2010-12-31 | 标准无保留意见 |
| 38962 | 900957 | 2012-12-31 | 标准无保留意见 |
| 38963 | 900957 | 2013-12-31 | 标准无保留意见 |
| 38964 | 900957 | 2014-12-31 | 标准无保留意见 |
| 38965 | 900957 | 2015-12-31 | 标准无保留意见 |
| 38966 | 900957 | 2016-12-31 | 标准无保留意见 |
38904 rows × 3 columns
In [340]:
areport_astype_manage.head()
Out[340]:
| 股票代码 | 截止日期 | 所属省份 | 行业名称 | |
|---|---|---|---|---|
| 2 | 1 | 2007-12-31 | 广东省 | 货币金融服务 |
| 3 | 1 | 2008-12-31 | 广东省 | 货币金融服务 |
| 4 | 1 | 2009-12-31 | 广东省 | 货币金融服务 |
| 5 | 1 | 2010-12-31 | 广东省 | 货币金融服务 |
| 6 | 1 | 2011-12-31 | 广东省 | 货币金融服务 |
In [341]:
# 只删除所有属性为NaN缺失值的样本。
# title = ["debt", "dev", "risk", "stock", "op", "manage", "audit", "violate", "cash", "nps", "net", "govern", "governother"]
areport_astype_debt.dropna(how="all", subset=["流动比率", "速动比率", "保守速动比率", "现金比率", "利息保障倍数B", "资产负债率",
"权益乘数", "产权比率", "权益对负债比率", "有形净值债务率"], inplace=True)
areport_astype_dev.dropna(how="all", subset=["固定资产增长率B","总资产增长率B","净资产收益率增长率B","净利润增长率B","利润总额增长率B",
"营业利润增长率B","营业收入增长率B","所有者权益增长率B"], inplace=True)
areport_astype_risk.dropna(how="all", subset=["财务杠杆","经营杠杆","综合杠杆"], inplace=True)
areport_astype_stock.dropna(how="all", subset=["股本总数","国有股","一般法人配售","已流通股份"], inplace=True)
areport_astype_op.dropna(how="all", subset=["应收账款与收入比","应收账款周转率B","存货与收入比","存货周转率B","应付账款周转率B",
"营运资金（资本）周转率B","现金及现金等价物周转率B","流动资产与收入比","流动资产周转率B",
"固定资产与收入比","固定资产周转率B","非流动资产周转率B","总资产周转率B",
"股东权益周转率B"], inplace=True)
areport_astype_manage.dropna(how="all", inplace=True)
areport_astype_audit.dropna(how="all", subset=["审计意见类型"], inplace=True)
areport_astype_violate.dropna(how="all", subset=["违规事件ID"], inplace=True)
areport_astype_cash.dropna(how="all", subset=["营业收入现金净含量","营业利润现金净含量","营运指数"], inplace=True)
areport_astype_net.dropna(how="all", subset=["资产报酬率B","总资产净利润率（ROA）B","净资产收益率B","营业利润率"], inplace=True)
areport_astype_govern.dropna(how="all", subset=["董事会会议次数","监事会会议次数","股东大会召开次数"], inplace=True)
areport_astype_governother.dropna(how="all", subset=["董事人数","其中：独立董事人数","监事总规模"], inplace=True)
In [342]:
# 下面开始连接数据集，主键是股票代码，截止日期。
# 通过对数据进行观察，考虑到：我们想保留全部的财务指标。故选择inner连接的结果。所以我们采用并且再加一步操作：删除inner连接后，
# 存在空值的样本。
merge_debt_dev = pd.merge(areport_astype_debt, areport_astype_dev, on=["股票代码", "截止日期"])
merge_debt_dev_risk = pd.merge(merge_debt_dev, areport_astype_risk, on = ["股票代码", "截止日期"])
merge_debt_dev_risk_stock = pd.merge(merge_debt_dev_risk, areport_astype_stock, on = ["股票代码", "截止日期"])
merge_debt_dev_risk_stock_op = pd.merge(merge_debt_dev_risk_stock, areport_astype_op, on = ["股票代码", "截止日期"])
merge_debt_dev_risk_stock_op_magange= pd.merge(merge_debt_dev_risk_stock_op, areport_astype_manage, on = ["股票代码", "截止日期"])
merge_debt_dev_risk_stock_op_magange_cash = pd.merge(merge_debt_dev_risk_stock_op_magange,
areport_astype_cash, on = ["股票代码", "截止日期"])
merge_debt_dev_risk_stock_op_magange_cash_net = pd.merge(merge_debt_dev_risk_stock_op_magange_cash,
areport_astype_net, on = ["股票代码", "截止日期"])
merge_debt_dev_risk_stock_op_magange_cash_net_govern = pd.merge(merge_debt_dev_risk_stock_op_magange_cash_net,
areport_astype_govern, on = ["股票代码", "截止日期"])
merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother = pd.merge(merge_debt_dev_risk_stock_op_magange_cash_net_govern,
areport_astype_governother, on = ["股票代码", "截止日期"])
In [343]:
# 最终获得一个inner连接的数据集。
print("inner样本集全部记录有",merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["股票代码"].count(),"条")
print("实际有",merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["股票代码"].drop_duplicates().count(), "家公司。")
print("最初debt样本集中有", areport_astype_debt["股票代码"].drop_duplicates().count(), "家公司。")
print("得到的样本一共有",len(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother.columns),"个字段。")
print("样本字段有：\n",merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother.columns)
inner样本集全部记录有 3111 条
实际有 494 家公司。
最初debt样本集中有 722 家公司。
得到的样本一共有 68 个字段。
样本字段有：
Index(['股票代码', '截止日期', '报表类型编码_x', '行业代码_x', '流动比率', '速动比率', '保守速动比率', '现金比率',
'利息保障倍数B', '资产负债率', '权益乘数', '产权比率', '权益对负债比率', '有形净值债务率', '报表类型编码_y',
'行业代码_y', '固定资产增长率B', '总资产增长率B', '净资产收益率增长率B', '净利润增长率B', '利润总额增长率B',
'营业利润增长率B', '营业收入增长率B', '所有者权益增长率B', '报表类型编码_x', '行业代码_x', '财务杠杆',
'经营杠杆', '综合杠杆', '股本总数', '国有股', '一般法人配售', '已流通股份', '报表类型编码_y', '行业代码_y',
'应收账款与收入比', '应收账款周转率B', '存货与收入比', '存货周转率B', '应付账款周转率B', '营运资金（资本）周转率B',
'现金及现金等价物周转率B', '流动资产与收入比', '流动资产周转率B', '固定资产与收入比', '固定资产周转率B',
'非流动资产周转率B', '总资产周转率B', '股东权益周转率B', '所属省份', '行业名称', '报表类型编码_x',
'行业代码_x', '营业收入现金净含量', '营业利润现金净含量', '营运指数', '报表类型编码_y', '行业代码_y',
'资产报酬率B', '总资产净利润率（ROA）B', '净资产收益率B', '营业利润率', '董事会会议次数', '监事会会议次数',
'股东大会召开次数', '董事人数', '其中：独立董事人数', '监事总规模'],
dtype='object', name=0)
In [344]:
# 删除重复列
try:
merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother.drop(["报表类型编码_x", "行业代码_x", "报表类型编码_y",
"行业代码_y"], axis=1, inplace=True)
except ValueError:
pass
print("一共有",len(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother.columns),"个字段。")
print("删除重复列后，样本字段有：\n",merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother.columns)
一共有 56 个字段。
删除重复列后，样本字段有：
Index(['股票代码', '截止日期', '流动比率', '速动比率', '保守速动比率', '现金比率', '利息保障倍数B', '资产负债率',
'权益乘数', '产权比率', '权益对负债比率', '有形净值债务率', '固定资产增长率B', '总资产增长率B',
'净资产收益率增长率B', '净利润增长率B', '利润总额增长率B', '营业利润增长率B', '营业收入增长率B',
'所有者权益增长率B', '财务杠杆', '经营杠杆', '综合杠杆', '股本总数', '国有股', '一般法人配售', '已流通股份',
'应收账款与收入比', '应收账款周转率B', '存货与收入比', '存货周转率B', '应付账款周转率B', '营运资金（资本）周转率B',
'现金及现金等价物周转率B', '流动资产与收入比', '流动资产周转率B', '固定资产与收入比', '固定资产周转率B',
'非流动资产周转率B', '总资产周转率B', '股东权益周转率B', '所属省份', '行业名称', '营业收入现金净含量',
'营业利润现金净含量', '营运指数', '资产报酬率B', '总资产净利润率（ROA）B', '净资产收益率B', '营业利润率',
'董事会会议次数', '监事会会议次数', '股东大会召开次数', '董事人数', '其中：独立董事人数', '监事总规模'],
dtype='object', name=0)
In [345]:
# 舞弊财报的选择标准:
# 1、审计结果意见为：拒绝表示意见，否定意见，无法表示意见的作为舞弊样本。
# 2、违规记录中有记录的，其中违规类型是：虚构利润，或虚列资产，且被罚款的，作为舞弊样本。被罚款意味着情节相对严重，以此作为标注舞弊样本
# 的依据。其它做删除处理。因为相关公司有舞弊动机的嫌疑但又没有足够证据。
# 3、时间不早于1999年。
# 4、其它的审计意见，如：有保留意见，或带有解释说明段的，定为无法确定是否舞弊的财报，即既不标注为舞弊，但也不标注为非舞弊。
# 5、对于审计意见为：标准无保留意见。另外参考财务数据齐全，利润*现金流>0的，定义为非舞弊样本。由于现在现金流为正，利润为负的经营模式
# 并非不存在，故如果这种样本数量并不占多数，则不必做删除处理，依然认为是非舞弊样本，但利润为正数，现金流为负数的项目除外需要删除。
# 6、将净资产收益率作为一个敏感指标，作为筛选财报是否舞弊的参考之一。将符合样本均值的99%置信区间范围内的样本作为非舞弊样本。
In [346]:
# 首先对净资产收益率B的统计描述
print("查看净资产收益率的统计描述：\n", merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"].describe())
# 我们用置信区间来考察净资产收益率的99%置信区间
count = merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"].count()
ave = merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"].mean()
var = merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"].var()
print("\n净资产收益率样本数；", count)
print("净资产收益率均值；", ave)
print("净资产收益率方差；", var)
min = merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"].min() # 净资产收益率最小值
max = merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"].max() # 净资产收益率最大值
# 99%的置信区间，双边t_0.01/2=2.5758
import math
t_005 = 2.5758
lower_99 = ave - t_005 * math.sqrt(var/count)
upper_99 = ave + t_005 * math.sqrt(var/count)
print("\n净资产收益率的99%置信区间是：", lower_99, upper_99)
print("\n在99%置信区间中的净资产收益率样本数：",merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"][
(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] >= lower_99) &
(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] <= upper_99)].count())
print("净资产收益率在-5至+5的样本数是：", merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"][
(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] >= -5) &
(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] <= 5)
].count())
print("净资产收益率在-1至+1的样本数是：", merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"][
(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] >= -1) &
(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] <= 1)
].count())
print("\n分割线***************************************************************************************************************分割线\n")
# 绘制净资产收益率B直方图
import matplotlib as mpl
mpl.rcParams['font.sans-serif']=['SimHei'] # 添加黑体字体
mpl.rcParams['axes.unicode_minus']=False # 显示负号
plt.figure(figsize=(16,9))
plt.xlabel("净资产收益率")
plt.ylabel("数量")
plt.title("净资产收益率直方图")
#bins_partial = np.arange(lower_99, upper_99, 0.001)
#bins_left = np.arange(min, lower_99,0.04)
#bins_right = np.arange(upper_99, max+0.01, 0.04)
#bins = np.concatenate([bins_left, bins_partial, bins_right]) # 根据净资产收益率的9全部数据区间绘制直方图。
# bins = np.arange(-5,5.01,0.02) # 根据上一步计算的净资产收益率在-5至+5区间内的样本数绘制直方图。
bins = np.arange(-1,1.01,0.02) # 根据上一步计算的净资产收益率在-5至+5区间内的样本数绘制直方图。
plt.hist(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"],
bins=bins)
plt.show()
查看净资产收益率的统计描述： count 3111.000000 mean 0.054651 std 0.595801 min -22.903987 25% 0.026819 50% 0.073488 75% 0.128509 max 6.417488 Name: 净资产收益率B, dtype: float64 净资产收益率样本数； 3111 净资产收益率均值； 0.054651202507232405 净资产收益率方差； 0.35497917851576993 净资产收益率的99%置信区间是： 0.027136579079059205 0.08216582593540561 在99%置信区间中的净资产收益率样本数： 915 净资产收益率在-5至+5的样本数是： 3104 净资产收益率在-1至+1的样本数是： 3059 分割线***************************************************************************************************************分割线
In [347]:
# 通过直方图，清晰看到净资产收益率>0时的样本明显增多。
print("净资产收益率>=0的样本数是：", merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"][
(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] >= 0) &
(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] <= 1)
].count())
print("净资产收益率<0的样本数是：", merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"][
(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] >= -1) &
(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] <= 0)
].count())
净资产收益率>=0的样本数是： 2733 净资产收益率<0的样本数是： 326
In [348]:
print("最小年份", merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["截止日期"].min())
print("最大年份", merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["截止日期"].max())
print("所得财报样本的时间区间符合筛选要求。")
最小年份 2007-12-31 00:00:00 最大年份 2016-12-31 00:00:00 所得财报样本的时间区间符合筛选要求。
In [349]:
# 接下来根据之前总结的财务知识，从审计结果和违规结果中寻找舞弊样本，进行标注。
# 过滤的样本集年份应在2007/12/31至2016/12/31之间。
areport_astype_audit_in_period = areport_astype_audit[(areport_astype_audit["截止日期"]>="2007-12-31") &
(areport_astype_audit["截止日期"]<="2016-12-31")].sort_values(
by=["股票代码", "截止日期"])
# 查看2007-12-31至2016-12-31期间的所有审计的财报的审计结果
print("审计结果有如下几类：\n",areport_astype_audit_in_period["审计意见类型"].drop_duplicates())
print("所得审计样本集的shape is：",areport_astype_audit_in_period.shape)
# 根据这个审计结果分类，我们将标准无保留意见作为非舞弊财报样本，无法发表意见作为舞弊财报样本。其它样本因为难以确认，做删除处理。
审计结果有如下几类： 30 标准无保留意见 111 无保留意见加事项段 112 保留意见 256 保留意见加事项段 408 无法发表意见 830 无保留意见加事项段 Name: 审计意见类型, dtype: object 所得审计样本集的shape is： (23635, 3)
In [350]:
# 继续从审计样本集中找到舞弊样本和非舞弊样本的划分依据：
# 1、将areport_astype_audit_in_period中审计意见类型为无法发表意见，且也是merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother
# 样本的，连接上一步做出的fraudulent_violate_in_period样本集，标注为舞弊样本。
# 2、将areport_astype_audit_in_period中审计意见类型为标准无保留意见，且也是merge_debt_dev_risk_stock_op_magange_cash_net_govern_
# governother样本的，且不在areport_astype_violate_in_period样本集中，且财务数据齐全，利润*现金流>0，净资产收益率不远离99%置信区间，
# 特别是不<0的，标注为非舞弊样本。
# 从审计样本集中找出无法发表意见的财报样本。
fraudulent = areport_astype_audit_in_period[(areport_astype_audit_in_period["审计意见类型"]=="无法发表意见")]
# 我们只需要股票代码和截止日期列。
fraudulent_set = fraudulent[["股票代码", "截止日期"]]
# 连接merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother样本集，标注审计意见类型为无法发表意见的样本为舞弊样本。
fra_label_set = pd.merge(fraudulent_set, merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother, on=["股票代码", "截止日期"])
fra_label_set
Out[350]:
| 股票代码 | 截止日期 | 流动比率 | 速动比率 | 保守速动比率 | 现金比率 | 利息保障倍数B | 资产负债率 | 权益乘数 | 产权比率 | ... | 资产报酬率B | 总资产净利润率（ROA）B | 净资产收益率B | 营业利润率 | 董事会会议次数 | 监事会会议次数 | 股东大会召开次数 | 董事人数 | 其中：独立董事人数 | 监事总规模 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 600747 | 2016-12-31 | 4.317858 | 4.216362 | 0.429126 | 0.007179 | -2.644175 | 0.205642 | 1.258879 | 0.258879 | ... | -0.028217 | -0.038888 | -0.048696 | -0.057097 | 8 | 0 | 3 | 7 | 3 | 3 |
| 1 | 600751 | 2007-12-31 | 0.099730 | 0.088491 | 0.062956 | 0.024068 | -5.169533 | 1.352631 | -2.835825 | -3.835825 | ... | -0.186198 | -0.222217 | 1.225354 | -0.407479 | 7 | 3 | 1 | 8 | 3 | 3 |
| 2 | 600757 | 2008-12-31 | 0.270523 | 0.196304 | 0.061112 | 0.011513 | -5.406312 | 1.448692 | -2.228703 | -3.228703 | ... | -0.375328 | -0.444752 | 2.771526 | -0.720218 | 11 | 3 | 2 | 8 | 3 | 5 |
| 3 | 600757 | 2009-12-31 | 0.161133 | 0.096746 | 0.043252 | 0.021410 | -2.705736 | 2.159602 | -0.862365 | -1.862365 | ... | -0.303778 | -0.416050 | 0.563787 | -0.998000 | 6 | 4 | 1 | 9 | 3 | 5 |
| 4 | 600771 | 2007-12-31 | 0.520096 | 0.481451 | 0.241823 | 0.017312 | -6.011429 | 1.567236 | -1.762934 | -2.762934 | ... | -0.287472 | -0.335293 | 6.417488 | -0.094552 | 8 | 4 | 4 | 13 | 5 | 2 |
| 5 | 600771 | 2008-12-31 | 0.474722 | 0.419529 | 0.163663 | 0.035937 | 4.028336 | 1.607853 | -1.645136 | -2.645136 | ... | 0.318346 | 0.239319 | -0.395770 | -1.131528 | 8 | 4 | 2 | 9 | 3 | 3 |
| 6 | 600771 | 2009-12-31 | 0.370831 | 0.319025 | 0.094344 | 0.022158 | -0.615014 | 1.893078 | -1.119722 | -2.119722 | ... | -0.041684 | -0.109461 | 0.142285 | -0.599570 | 6 | 3 | 3 | 9 | 3 | 3 |
| 7 | 600806 | 2016-12-31 | 0.975833 | 0.464423 | 0.402529 | 0.199014 | -3.361667 | 0.807768 | 5.202056 | 4.202056 | ... | -0.072584 | -0.094176 | -0.423256 | -0.722232 | 19 | 2 | 4 | 12 | 4 | 4 |
| 8 | 600817 | 2007-12-31 | 1.012930 | 1.011474 | 1.010805 | 0.000907 | -1.274907 | 0.966228 | 29.610340 | 28.610340 | ... | -0.028810 | -0.051408 | -0.736964 | -0.049137 | 9 | 5 | 1 | 9 | 3 | 3 |
| 9 | 600817 | 2008-12-31 | 0.002029 | 0.001980 | 0.001742 | 0.000730 | -57.860341 | 20.246714 | -0.051957 | -1.051957 | ... | -0.816729 | -0.830845 | 2.295542 | -100.895035 | 8 | 3 | 1 | 6 | 2 | 3 |
| 10 | 600817 | 2009-12-31 | 0.003938 | 0.003936 | 0.003751 | 0.003352 | -113.902467 | 27.919909 | -0.037147 | -1.037147 | ... | -2.975073 | -3.001192 | 0.132242 | -0.984071 | 8 | 3 | 4 | 8 | 3 | 3 |
| 11 | 600988 | 2008-12-31 | 0.218884 | 0.171296 | 0.163674 | 0.012413 | -0.831288 | 1.100881 | -9.912711 | -10.912711 | ... | -0.049032 | -0.108015 | 2.350989 | -0.589627 | 9 | 3 | 3 | 5 | 2 | 1 |
12 rows × 56 columns
In [351]:
# 再从违规处理样本集找舞弊的样本，依然筛选2007-12-31至2016-12-31期间的样本。
areport_astype_violate_in_period = areport_astype_violate[(areport_astype_violate["处理文件日期"] >="2007-12-31") &
(areport_astype_violate["处理文件日期"] <="2016-12-31")].sort_values(
by=["股票代码", "处理文件日期"])
# 按照前面的财务知识，将违规类型为：虚构利润，或虚列资产，且处罚金额>0的，标注为舞弊样本。之后再与merge_debt_dev_risk_stock_op_magange
# _cash_net_govern_governother样本inner连接，再合并审计样本集中筛选出来的舞弊样本，作为舞弊样本。
try:
violate = areport_astype_violate_in_period["违规类型"].str.split(",", expand=True)
violate.columns = ["违规类型"]*violate.columns.size
except KeyError:
pass
mask = violate.isin(["虚构利润", "虚列资产"])
# 获得虚构利润和虚列资产的样本，但还需要后面与areport_astype_violate_in_period样本集连接起来。
violate_mask = violate[mask].dropna(how="all")
areport_astype_violate_in_period.drop(["违规类型"], axis=1,inplace=True)
violate_in_period = areport_astype_violate_in_period.loc[violate_mask.index]
violate_mask_in_period = pd.concat([violate_in_period, violate_mask], axis=1)
fraudulent_violate = violate_mask_in_period[(violate_mask_in_period["处罚金额-上市公司"]>0)]
print("筛选出的虚列利润，或虚列资产，且受到罚款的样本数为：", fraudulent_violate["违规事件ID"].size)
# 对fraudulent_violate的违规年度进行筛选，只需要2007-2016年度的样本记录
fraudulent_violate_in_period_mask = fraudulent_violate["违规年度"].isin([2007,2008,2009,2010,2011,2012,2013,2014
,2015,2016])
# 筛选出在2007-2016年间违规行为的样本。
fraudulent_violate_in_period_mask.dropna(how="all", inplace=True)
f_violate_in_period = fraudulent_violate["违规年度"][fraudulent_violate_in_period_mask].fillna(0).astype("int")
fraudulent_violate_for_concat = fraudulent_violate.drop(["违规年度"], axis=1)
fraudulent_violate_in_period = pd.concat([fraudulent_violate_for_concat, f_violate_in_period], axis=1)
fraudulent_violate_in_period["违规年度"]
# 因为违规样本的处理文件日期和公告日期，并不是实际相关财报舞弊的年月日期，所以需要逐条从违规行为中查阅具体的违规年月日期，然后与股票
# 代码连接，形成一个“股票代码-违规日期”样本集，该样本集将与审计样本集中筛选出的舞弊样本集（也只需要股票代码-截止日期列）连接，最终
# 与merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother样本集inner连接，得到最终需要的舞弊样本训练集。
# 首先通过fraudulent_violate_in_period["股票代码"]从merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother样本集中筛选出舞弊
# 的公司样本。
f_vio_stock_code = pd.DataFrame(fraudulent_violate_in_period["股票代码"])
#fraudulent_company = pd.merge(f_vio_stock_code, merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother
# ,how="inner", on=["股票代码"])
fraudulent_company = pd.merge(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother, f_vio_stock_code
,how="inner", on=["股票代码"])
# 逐条人工查看违规企业的违规年度和违规行为，并人工筛选生成违规财报的截止日期。
# 筛选出merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother样本集中违规企业的股票代码。
fraudulent_company_id = fraudulent_company["股票代码"].drop_duplicates()
intersection_fraudulent_violate_in_period = fraudulent_violate_in_period[
fraudulent_violate_in_period["股票代码"].isin(fraudulent_company_id)]
intersection_fraudulent_violate_in_period[["股票代码", "违规年度"]]
# 经逐条查阅违规行为，将600800相关记录从merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother样本集中删除，
# 原因是违规行为涉嫌舞弊的证据不是十分充分。既不能将其作为舞弊样本，也不能将其作为非舞弊样本。
fraudulent_violate_records = {"股票代码": [600810, 600810, 600822, 600822, 600822, 600822, 600822, 600986, 600986, 600986, 601519,
601558],
"截止日期": ["2014-12-31", "2015-6-30", "2008-12-31", "2009-12-31", "2010-12-31", "2011-12-31",
"2012-12-31", "2007-12-31", "2008-12-31", "2009-6-30", "2013-12-31", "2011-12-31"]}
# 人工从违规样本集中找出舞弊财报样本。
fraudulent_violate_set = pd.DataFrame(fraudulent_violate_records, columns=["股票代码", "截止日期"])
fraudulent_violate_set["截止日期"] = pd.to_datetime(fraudulent_violate_set["截止日期"])
print("\nfraudulent_violate_set的数据类型:\n", fraudulent_violate_set.dtypes)
# 从merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother样本集中删除人工发现的股票代码=600800的样本记录。
company_stock_code_600800 = merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother[
merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["股票代码"]==600800].index
merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother.drop(labels=company_stock_code_600800, inplace=True)
# 将获得的舞弊股票代码-截止日期与merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother相关属性进行inner连接，得到从违规样本
# 集中筛选出的舞弊样本。
fraudulent_violate_sample = pd.merge(
merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother, fraudulent_violate_set, on=["股票代码", "截止日期"])
print("\n从违规样本集中人工筛选出的舞弊样本集shape是：", fraudulent_violate_sample.shape)
筛选出的虚列利润，或虚列资产，且受到罚款的样本数为： 58 fraudulent_violate_set的数据类型: 股票代码 int64 截止日期 datetime64[ns] dtype: object 从违规样本集中人工筛选出的舞弊样本集shape是： (10, 56)
In [352]:
# 将之前从违规样本集中得到的舞弊样本集fraudulent_violate_sample，和从审计样本集中得到的舞弊样本集fra_label_set合并，得到的总的舞弊样本集。
fraudulent_set = pd.concat([fraudulent_violate_sample, fra_label_set], ignore_index=True)
# 给舞弊样本集增加标签列，标签值为-1，表示舞弊类型。
fraudulent_set["label"] = -1
print("按照前述的判断舞弊样本的业务知识，从源数据中最终得到的舞弊样本shape是：", fraudulent_set.shape) # 得到舞弊样本的训练集。
按照前述的判断舞弊样本的业务知识，从源数据中最终得到的舞弊样本shape是： (22, 57)
In [353]:
# 从审计样本集中找出标准无保留意见的财报样本，并只保留股票代码和截止日期属性。
normal_audit = areport_astype_audit_in_period[(areport_astype_audit_in_period["审计意见类型"]=="标准无保留意见")]
print("标准无保留意见的审计样本集shape是：：", normal_audit.shape)
# 将上一步所得数据集，与merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother样本集inner连接
normal_select = pd.merge(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother, normal_audit, on=["股票代码", "截止日期"])
print("inner筛选后的标准无保留意见样本数目为：", normal_select.index.size)
标准无保留意见的审计样本集shape是：： (22527, 3) inner筛选后的标准无保留意见样本数目为： 2963
In [354]:
# 考虑到现代企业经营中，现金流>0，但利润<0的例子也越来越多。所以最终我们只筛选出现金流为负，但利润为正的财报样本，将这些样本删除。
delete_index = normal_select[(normal_select["营业收入现金净含量"]<0) & (normal_select["营业利润现金净含量"]>0)].index
# 删除上述样本
normal_select.drop(labels=delete_index, inplace=True)
print("最终得到非舞弊样本数目：", normal_select[["营业收入现金净含量", "营业利润现金净含量"]].index.size)
最终得到非舞弊样本数目： 2788
In [374]:
# 现在再检查下筛选得到的normal_select样本是否出现在违规样本中。这种检验比较好的方法是将normal_select样本集和从违规样本集中得到的
# fraudulent_violate_set的股票代码和截止日期转换成multiindex，然后用index.isin()找到是哪个index重复出现在两个样本集中。
# 先将fraudulent_violate_set的股票代码和截止日期转换成multiindex。
idx_array_check_fraudulent_violate = [list(fraudulent_violate_set["股票代码"]),list(fraudulent_violate_set["截止日期"])]
tuples_idx_array_check_fraudulent_violate = list(zip(*idx_array_check_fraudulent_violate))
idx_check_fraudulent_violate = pd.MultiIndex.from_tuples(tuples_idx_array_check_fraudulent_violate,names=["股票代码", "截止日期"])
# 再将normal_select样本的股票代码和截止日期转换成multiindex。
normal_select_check = normal_select.iloc[:,2:]
idx_array_check_normal = [list(normal_select["股票代码"]), list(normal_select["截止日期"])]
tuples_idx_array_check_normal = list(zip(*idx_array_check_normal))
idx_check_normal = pd.MultiIndex.from_tuples(tuples_idx_array_check_normal, names=["股票代码", "截止日期"])
normal_select_check.index = idx_check_normal
# 检查normal_select_check样本集中是否有idx_check_fraudulent_violate对应的样本（即舞弊，样本）。
normal_select[idx_check_normal.isin(idx_check_fraudulent_violate)]
# 最终发现股票代码=601558，截止日期=2011-12-31的样本既出现在normal_select样本集中，也出现在违规舞弊样本中。所以将其从normal_select中
# 删除，归为舞弊样本。
normal_select.drop(labels=normal_select[idx_check_normal.isin(idx_check_fraudulent_violate)].index, inplace=True)
# 将“审计意见类型”列换成标签+1。
try:
# normal_select_weight指权重最大的非舞弊样本，因其评审条件最严格，是最可靠的非舞弊样本。
normal_select = normal_select.drop(labels=["审计意见类型"], axis=1)
except KeyError:
pass
normal_select["label"] = 1
print("得到的非舞弊样本shape是：", normal_select_weight.shape)
得到的非舞弊样本shape是： (2780, 57)
In [500]:
# 检查所得的非舞弊样本和舞弊样本是否存在空值
print("删除空值后，非舞弊样本shape：", normal_select.dropna().shape, "\n删除空值后，舞弊样本shape：",fraudulent_set.dropna().shape)
# 发现都有所减少。进一步查看各字段空值情况。如果仅有少量字段有空值，则考虑用该类样本该指标的均值代替。否则删除空值。
cols = ['流动比率', '速动比率', '保守速动比率', '现金比率', '利息保障倍数B', '资产负债率',
'权益乘数', '产权比率', '权益对负债比率', '有形净值债务率', '固定资产增长率B', '总资产增长率B',
'净资产收益率增长率B', '净利润增长率B', '利润总额增长率B', '营业利润增长率B', '营业收入增长率B',
'所有者权益增长率B', '财务杠杆', '经营杠杆', '综合杠杆', '股本总数', '国有股', '一般法人配售', '已流通股份',
'应收账款与收入比', '应收账款周转率B', '存货与收入比', '存货周转率B', '应付账款周转率B', '营运资金（资本）周转率B',
'现金及现金等价物周转率B', '流动资产与收入比', '流动资产周转率B', '固定资产与收入比', '固定资产周转率B',
'非流动资产周转率B', '总资产周转率B', '股东权益周转率B', '营业收入现金净含量','营业利润现金净含量', '营运指数',
'资产报酬率B', '总资产净利润率（ROA）B', '净资产收益率B', '营业利润率', '董事会会议次数', '监事会会议次数',
'股东大会召开次数', '董事人数', '其中：独立董事人数', '监事总规模']
print("非舞弊样本空值情况：\n")
for col in cols:
if normal_select[col].isna().any():
print("带空值的字段名是：", col)
print("舞弊样本空值情况：\n")
for col in cols:
if fraudulent_set[col].isna().any():
print("带空值的字段名是：", col)
# 根据检查空值结果，对舞弊样本的“有形净值债务率”填充mean值。非舞弊样本的空值全部删除。
normal_select.dropna(inplace=True) # 删除非舞弊样本的空值
fraudulent_set["有形净值债务率"] = fraudulent_set["有形净值债务率"].fillna(value=fraudulent_set["有形净值债务率"].mean())
删除空值后，非舞弊样本shape： (2589, 57) 删除空值后，舞弊样本shape： (22, 57) 非舞弊样本空值情况： 舞弊样本空值情况：
In [477]:
# 再考虑对净资产收益率指标进行评估，我们选择净资产收益率的置信度为0.99的normal_select中样本作为非舞弊样本。
mean_normal = normal_select["净资产收益率B"].mean()
std_normal = normal_select_weight["净资产收益率B"].std()
print("非舞弊样本的净资产收益率的标准差：", std_normal)
Z_t001_df_infinite = 2.5758 # 置信度0.99，t分布下alpha=0.01
mask_normal_net = (normal_select["净资产收益率B"]>=(mean_normal-Z_t001_df_infinite*std_normal)) & \
(normal_select["净资产收益率B"]<=(mean_normal+Z_t001_df_infinite*std_normal))
# 最终得到经严格评估筛选出来的非舞弊样本。
normal = normal_select[mask_normal_net]
print("最终得到的非舞弊样本shape是：", normal.shape)
# 查看筛选得到的非舞弊样本的净资产收益率统计描述。
print("非舞弊样本的净资产收益率统计描述：\n", normal_select_weight["净资产收益率B"].describe())
min_normal = normal["净资产收益率B"].min()
max_normal = normal["净资产收益率B"].max()
count_normal = normal["净资产收益率B"].count()
# 查看非舞弊样本的净资产收益率直方图。
plt.figure(figsize=(16,9)) # 定义图表的尺寸，单位inch。
plt.xlabel("净资产收益率")
plt.ylabel("数目")
plt.title("净资产收益率直方图")
bins = np.arange(min_normal, max_normal+0.001, 0.01)
plt.hist(normal["净资产收益率B"], bins=bins, color="steelblue")
plt.show()
# 从直方图看到，按照净资产收益率指标筛选非舞弊样本后，非舞弊样本总数减少120个。统计分布总体包络似乎依然符合正态分布。
# 净资产收益率>0的区间内，样本数量更多。这意味着更多非舞弊企业的净资产收益率为正数。
# 在严格评估的非舞弊样本中，净资产收益率0-0.3之间，与现实情况比较吻合。
非舞弊样本的净资产收益率的标准差： 0.10098230116102716 最终得到的非舞弊样本shape是： (2492, 57) 非舞弊样本的净资产收益率统计描述： count 2761.000000 mean 0.088913 std 0.100982 min -0.554535 25% 0.039380 50% 0.080221 75% 0.133136 max 0.766859 Name: 净资产收益率B, dtype: float64
In [478]:
# 查看非舞弊样本企业所在省份的分布
plt.figure(figsize=(16,9))
province_normal = normal["所属省份"].drop_duplicates()
province_count = province_normal.size
province_normal.index = np.arange(province_count)
y_label_count = [] # 定义各省份的企业数目，作为柱状图的y轴数值使用。
for i in range(province_count):
y_label_count.append(normal["所属省份"][normal["所属省份"]==province_normal[i]].count())
# 映射省份index与省份名称，为方便各省份企业数目的排序。
map_province_company_count= pd.DataFrame({"province": province_normal, "count": y_label_count})
map_province_company_count.sort_values(by=["count"], ascending=False, inplace=True)
map_province_company_count.index = np.arange(province_count)
plt.xlabel("省份")
plt.ylabel("企业数目")
plt.title("非舞弊企业样本数目在各省份的分布")
plt.bar(map_province_company_count.index, map_province_company_count["count"], color="steelblue",
tick_label=map_province_company_count["province"])
plt.show()
In [501]:
# 查看舞弊企业的行业分布
fraudulent_industry = fraudulent_set["行业名称"].drop_duplicates()
fraudulent_industry.index = np.arange(fraudulent_industry.index.size)
fraudulent_industry_count = []
for i in range(fraudulent_industry.index.size):
fraudulent_industry_count.append(fraudulent_set["行业名称"][(fraudulent_set["行业名称"])==fraudulent_industry[i]].count())
fraudulent_industry_map = pd.DataFrame({"industry":fraudulent_industry, "count":fraudulent_industry_count})
fraudulent_industry_map.sort_values(by=["count"], ascending=False, inplace=True)
fraudulent_industry_map.index = np.arange(fraudulent_industry.index.size)
# 绘制舞弊样本在各行业分布的柱形图
plt.figure(figsize=(16,9))
plt.xlabel("行业")
plt.ylabel("样本数目")
plt.title("舞弊样本在各行业分布的柱形图")
plt.bar(fraudulent_industry_map.index, fraudulent_industry_map["count"], color="steelblue",
tick_label=fraudulent_industry_map["industry"])
plt.show()
In [582]:
# 将之前得到的normal_select_weight样本集和fraudulent_set样本集合并，作为之后训练分类模型使用的样本集。
merge_samples = pd.concat([normal, fraudulent_set], ignore_index=True)
try:
merge_samples.drop(labels=["所属省份", "行业名称"], axis=1, inplace=True)
except KeyError:
pass
print("总体样本merge_samples的shape是：", merge_samples.shape)
总体样本merge_samples的shape是： (2514, 55)
In [596]:
# 将总体样本merge_samples划分为训练集和测试集，按照1：1比例划分。
train_set = merge_samples.iloc[::2,:]
test_set = merge_samples.iloc[1:merge_samples.shape[0]:2,:]
train_set.reset_index(drop=True, inplace=True)
test_set.reset_index(drop=True, inplace=True)
print("训练集shape：",train_set.shape, "\n测试集shape：",test_set.shape)
# 至此样本清洗工作完成。 清洗好的数据应当保存为文本文件，便于后续反复训练模型使用， 无需再重复清洗数据。
训练集shape： (1257, 55) 测试集shape： (1257, 55)
In [408]:
# 从这一步开始，将使用SVM，Logisitic Regression，Naive Bayes三种分类器训练样本，评估分类效果，并最终选择最适合的模型。
In [ ]:
In [942]:
from sklearn.svm import SVC
from sklearn import preprocessing
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
# 定义一个针对ndarray的统计函数
def stat(df):
'''利用numpy统计模块，计算ndarray数据结构的统计指标。包括：min，max，mean，median，var，std，CV（变异系数），Z-score。
其它有必要的函数需要时再补充。
其中CV（变异系数） = std/mean
Z-store = (value - mean(value))/std(value)，是一种标准化数据的方法。
传入的obj是一个DataFrame。'''
ndarray = np.array(df)
if len(ndarray.shape)>=2:
dmin = np.min(ndarray, axis=0)
dmax = np.max(ndarray, axis=0)
dmean = np.mean(ndarray, axis=0)
dmedian = np.median(ndarray, axis=0)
dvar = np.var(ndarray, axis=0)
dstd = np.std(ndarray, axis=0)
CV = np.divide(dstd, dmean)
index = ["min", "max", "mean", "median", "var", "std", "CV"]
columns = df.columns
output = pd.DataFrame([dmin, dmax, dmean, dmedian, dvar, dstd, CV], index=index, columns=columns)
elif len(ndarray.shape) == 1:
dmin = np.min(ndarray)
dmax = np.max(ndarray)
dmean = np.mean(ndarray)
dmedian = np.median(ndarray)
dvar = np.var(ndarray)
dstd = np.std(ndarray)
CV = np.divide(dstd, dmean)
index = ["min", "max", "mean", "median", "var", "std", "CV"]
name = df.name
output = pd.Series([dmin, dmax, dmean, dmedian, dvar, dstd, CV], index=index, name=name )
else:
return "传入数据有误，请检查是否是ndarray数据结构。"
return output
# 对训练数据预处理的方法，有信噪比和fisher分数两种方法。
# SN=abs((mean(attr_t)-mean(attr_f))/(std(attr_t)+std(attr_f)))，即某个属性的正例样本与负例样本的均值之差的绝对值，除以该属性的正例
# 样本与负例样本的标准差之和。
# 为尽可能覆盖总体样本数值的特征，首先我们将标注好分类标签的训练集train_svm和验证集test合并起来，它们都是dataFrame。
# 定义SN信噪比函数
def sn(df, ascending=False):
'''输入参数df是一个带有分类标签label字段的DataFrame。
df的shape是：n_samples*n_features.
ascending参数=False时，计算好的SN值从大到小排序。True时按从小到大排序。'''
df_drop_label = df.drop(labels=['label'], axis=1)
true_set = df_drop_label[df["label"]==1]
false_set = df_drop_label[df["label"]==-1]
# 分别计算正负例样本的均值和标准差。
mean_t = true_set.mean(axis=0)
mean_f = false_set.mean(axis=0)
std_t = true_set.std(axis=0)
std_f = false_set.std(axis=0)
SN = abs(mean_t - mean_f) / (std_t + std_f)
SN.sort_values(axis = 0, ascending=False, inplace=True)
return SN
# 另一种选择是计算fisher值。fisher值与SN值类似，但是有时它们计算的特征“重要性”并不完全相同。
def fishvalue(df, ascending=False):
'''输入参数df是一个带有分类标签label字段的DataFrame。
df的shape是：n_samples*n_features.
ascending参数=False时，计算好的SN值从大到小排序。True时按从小到大排序。
reeturn以df的列名为index，fisher value为值的Series。'''
df_drop_label = df.drop(labels=['label'], axis=1)
true_set = df_drop_label[df["label"]==1]
false_set = df_drop_label[df["label"]==-1]
#print(false_set.shape, true_set.shape)
# 分别计算正负例样本的均值和标准差。
mean_t = true_set.mean(axis=0)
mean_f = false_set.mean(axis=0)
std_t = true_set.std(axis=0)
std_f = false_set.std(axis=0)
fvalue = abs(mean_t - mean_f) / np.sqrt(np.power(std_t,2) + np.power(std_f,2))
fvalue.sort_values(axis = 0, ascending=ascending, inplace=True)
return fvalue
# 准备CV交叉验证用的训练数据
# 定义CV交叉验证函数。一般取n_splits=10, test_size=0.1
def CV_set(n_splits, train_size, test_size, random_state=0):
'''定义CV交叉验证函数。X为输入的样本向量，y为样本的分类标签。其中n_split指将原数据集分割几次，一般选5到10次。train_size或test_size
指K-fold中的K，即将一个数据集分成几折，一般有10折(test_size=0.1)，7折(test_size=0.3)。但要注意，在StratifiedShuffleSplit()函数中，
train_size和test_size的个数不能少于2。random_state=int可以确保每次随机策略分割的数据集不重复，因为random随机数种子固定了。所谓
stratified，指测试集中总是会保留有尽量符合源数据集中正负比例的正负例样本。'''
cv = StratifiedShuffleSplit(n_splits=n_splits, train_size=train_size, test_size=test_size, random_state=random_state)
#cv.get_n_splits(X,y)
return cv
# 对训练集进行交叉验证grid search计算。
def grid(X, y, svc, parameters, cv):
'''对训练集进行grid search计算。'''
scores = ['precision', 'recall']
# 从训练集中按分割好的K-fold，提取出分割好的训练验证集和测试验证集。
assert isinstance(cv, StratifiedShuffleSplit), \
"我们暂时强制必须使用sklearn.model_selection._split.StratifiedShuffleSplit"
for score in scores:
print("针对%s这个指标进行参数grid search。"%score)
clf = GridSearchCV(svc, parameters, cv=cv, n_jobs=2, scoring='%s_macro'%score, return_train_score=True)
# 当cv参数是将训练集分割好的cross-validation generator object时。
for train_idx, test_idx in cv.split(X,y):
X_train_cv, X_test_cv = X[train_idx], X[test_idx]
y_train_cv, y_test_cv = y[train_idx], y[test_idx]
clf.fit(X_train_cv, y_train_cv)
# 只考察grid search中test_score的结果：
means = clf.cv_results_['mean_test_score']
stds = clf.cv_results_['std_test_score']
print("在交叉验证集下Grid计算各参数下的测试集的Score值：\n")
for mean, std, params in zip(means, stds, clf.cv_results_['params']):
print("%0.3f (+/-%0.03f) for %r"% (mean, std * 2, params))
print("CV交叉验证，分类结果如下报告：")
y_true, y_pred = y_test_cv, clf.predict(X_test_cv)
'''
print("检查y_pred中是否只有正例或反例。\
这导致warning:UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\
出现。\n",np.unique(y_pred))
print("检查y_true中是否有超过+/-1的奇怪的值。\
这导致warning出现。\n",np.unique(y_true))
'''
print("交叉验证集真实标签有：", np.unique(y_true))
print("交叉验证集预测标签有：", np.unique(y_pred))
print("检查交叉验证集中测试集标签是否有预测不到的值：", set(y_true) - set(y_pred))
# F1 = 2*(precision * recall)/(precision + recall)
print("得到的分类结果评估报告如下：\n", classification_report(y_true, y_pred))
# 查看cv交叉验证结果。
print("本轮grid search结果，得到最好的参数选择是：", clf.best_params_)
print("本轮grid search结果，最好的参数对应的真正测试集上的score是：", clf.best_score_)
return (clf.best_params_, clf.best_score_)
# 以grid search的最优结果，选最优参数kernel，C，gamma重新训练样本。
def svc_op(X, y, class_weight, **kwargs):
'''用grid search找到的最优参数C和gamma，训练最终的SVC模型。'''
#if kernel == 'rbf':
# clf = SVC(class_weight=class_weight, **kwargs)
#elif kernel == 'linear':
# clf = SVC(class_weight='balanced', **kwargs)
clf = SVC(class_weight=class_weight, random_state=0, **kwargs)
clf.fit(X,y)
return clf
# 计算训练模型clf_op的AUC值，评估模型质量。
from sklearn.metrics import roc_auc_score
def auc(y_test_orgin, y_pred):
auc = roc_auc_score(y_test_orgin, y_pred)
return auc
# 将SVC模型调优的过程封装到函数中实现自动化操作。这一版调整超参的方法是：将grid search分成粗细search两部分。细部分是沿用之前的思路：
# 一旦发现超参到达最大/最小边界，则直接重新以该最大/最小点为中心，重新设置超参的范围。从代码看，这一版思路更严谨，因为时刻比较了2种
# kernel的grid search结果。但是通过grid search试验发现，仅仅有细search不够，因为初始超参网格{C,gamma}未必是合适的超参范围，在给定的
# 超参范围内找到score最大的超参组合，该组合未必是朝着优化超参的方向变化。深入思考可以发现：实际上初始超参究竟选在哪个点，哪个范围，
# 并没有什么办法可以预先判断。唯一能做的就是多设几个超参，分别计算，然后选择score最高的，且score朝着更高方向变化的超参。所以在细search
# 之前增加粗search步骤。粗search步骤采用5点法，即[{C=1,gamma=1}, {C=10^3, gamma=10^3}, {C=10^-3, gamma=10^3}, {C=10^3, gamma=10^-3},
# {C=10^-3, gamma=10^-3}]，选这5个点中最高score的点，以其为中心点，左右5个幂指数步长展开为超参范围。
def coarse_tunemodel(X_train, y_train, class_weight_option, cv, coarse_grid_parameter_boundry=3, coarse_grid_parameter_step_num=3):
'''针对SVM模型，将训练模型的过程封装到函数中实现自动化操作，对模型参数不断调优，直至模型参数不再变化。
X_train是不带分类标签的训练集，y_train是对应train的样本分类标签值。class_weight_option是class_weight组成的字典，它分为kernel=rbf和
kernel=linear两种，示例：class_weight_option={'rbf':{-1:5}, 'linear':{-1:20}}。cv是交叉验证类的实例，
目前我们一般选StratifiedShuffleSplit()。coarse_grid_parameter_boundry=3和coarse_grid_parameter_step_num=3指粗grid search中用到的
生成超参范围的参数，我们目前使用函数np.logspace(start, end, num, base=10)生成粗grid search超参范围。
其中end=coarse_grid_parameter_boundry=3，start是end取负数。num=coarse_grid_parameter_step_num。建议num取3，
因为粗grid search不必太多，5个点或9个点的遍历计算应该很合适。
'''
# 先进行粗略的grid search，即coarse grid search。
parameter_coarse_rbf = {'kernel':['rbf'], 'C':np.concatenate(([1],np.logspace(-3,3,2))),
'gamma':np.concatenate(([1],np.logspace(-3,3,2)))} # 首选C=1， gamma=1的超参。
parameter_coarse_linear = {'kernel':['linear'], 'C':np.concatenate(([1],np.logspace(-3,3,2)))}
svc_rbf = SVC(class_weight=class_weight_option['rbf'], random_state=0)
svc_linear = SVC(class_weight=class_weight_option['linear'], random_state=0)
grid_argument_coarse_rbf = grid(X_train, y_train, svc_rbf, parameter_coarse_rbf, cv) # 分别计算rbf和linear核函数的最佳超参初始点。
grid_argument_coarse_linear = grid(X_train, y_train, svc_linear, parameter_coarse_linear, cv)
tuned_argument_rbf = grid_argument_coarse_rbf[0] # 计算最好的超参组合。
tuned_argument_linear = grid_argument_coarse_linear[0]
print("RBF的粗grid search最佳超参：", tuned_argument_rbf,
"RBF的粗grid search最高分值：", grid_argument_coarse_rbf[1])
print("linear的粗grid search最佳超参：", tuned_argument_linear,
"linear的粗grid search最高分值：", grid_argument_coarse_linear[1])
# 在粗search基础上进行细微的search，即subtle grid search。
# 以上一步得到的粗调超参为中心，创建新的超参范围parameter。
rbf_C_grid_orgin = tuned_argument_rbf['C'] # 初始化超参范围的中点
rbf_gamma_grid_orgin = tuned_argument_rbf['gamma']
linear_C_grid_orgin = tuned_argument_linear['C']
# 根据超参中点创建用于grid search的超参范围。
parameter_orgin = [{'kernel':['rbf'], 'C':np.logspace(np.log10(rbf_C_grid_orgin)-5, np.log10(rbf_C_grid_orgin)+5, 11),
'gamma':np.logspace(np.log10(rbf_gamma_grid_orgin)-5, np.log10(rbf_gamma_grid_orgin)+5, 11)},
{'kernel':['linear'], 'C':np.logspace(np.log10(linear_C_grid_orgin)-5, np.log10(linear_C_grid_orgin)+5, 11)}]
return parameter_orgin
def subtle_tunemodel(X_train, y_train, class_weight_option, parameter, cv):
'''针对SVM模型，将训练模型的过程封装到函数中实现自动化操作，对模型参数不断调优，直至模型参数不再变化。'''
# 对不同kernel类型分开计算grid search。
svc_rbf = SVC(class_weight=class_weight_option['rbf'], random_state=0)
svc_linear = SVC(class_weight=class_weight_option['linear'], random_state=0)
grid_argument_subtle_rbf = grid(X_train, y_train, svc_rbf, parameter, cv)
grid_argument_subtle_linear = grid(X_train, y_train, svc_linear, parameter, cv)
# 将各kernel类型的grid search得到的score汇总在list里。
grid_argument_subtle_scores = [grid_argument_subtle_rbf[1], grid_argument_subtle_linear[1]]
grid_argument_subtle_best_param = [grid_argument_subtle_rbf[0], grid_argument_subtle_linear[0]]
tuned_argument = grid_argument_subtle_best_param[np.argmax(grid_argument_subtle_scores)] # 选score最高对应的best parameter。
new_kernel = tuned_argument['kernel']
best_score = np.max(grid_argument_subtle_scores)
assert (isinstance(parameter, list) or isinstance(parameter, dict)), "sklearn.svm的grid_search()中的参数应为dict或list。"
# 将超参的情况分为4种。1、C是边界点，gamma不是边界点。这种情况只更新C，更新方法为以上一步得到的新的C值为中点，分别向左右
# 减5个步长，base=10.这是一种完全初始化的方法。2、C是边界点，gamma也是边界点，则在gamma重新设置步骤，
# 完全更新了超参{C,gamma}为新值。3、C不是边界点，gamma是边界点，则在gamma步骤重新设置超参，完全更新超参{C,gamma}为新值。4、
# C和gamma都不是边界点，则在gamma步骤，按照找到的C和gamma各自的新边界点，设置新的超参{C, gamma}。
try:
if new_kernel == 'rbf':
new_C_grid_center = tuned_argument['C'] # 将新计算得到的最优C值作为下一次grid计算的C值的中点。
new_gamma_grid_center = tuned_argument['gamma'] # 将新计算得到的最优gamma值作为下一次grid计算的gamma值的中点。
if new_C_grid_center == np.min(parameter['C']) or new_C_grid_center == np.max(parameter['C']):
print("发现最优参数C为原先的最大/最小值，直接重新设置超参。")
update_C = {'C':np.logspace(np.log10(new_C_grid_center)-5, np.log10(new_C_grid_center)+5,11)}
else:
new_C_grid_interval_left_endpoint = np.max(parameter['C'][parameter['C']<new_C_grid_center])
new_C_grid_interval_right_endpoint = np.min(parameter['C'][parameter['C']>new_C_grid_center])
update_C = {'C':np.logspace(np.log2(new_C_grid_interval_left_endpoint),
np.log2(new_C_grid_interval_right_endpoint), 11, base=2)}
if new_gamma_grid_center == np.min(parameter['gamma']) or new_gamma_grid_center == np.max(parameter['gamma']):
print("发现最优参数gamma为原先的最大/最小值，直接重新设置超参。")
new_parameter = {'kernel':[new_kernel],
'C':update_C['C'],
'gamma':np.logspace(np.log10(new_gamma_grid_center)-5, np.log10(new_gamma_grid_center)+5, 11)}
else:
new_gamma_grid_interval_left_endpoint = np.max(parameter['gamma'][parameter['gamma']<new_gamma_grid_center])
new_gamma_grid_interval_right_endpoint = np.min(parameter['gamma'][parameter['gamma']>new_gamma_grid_center])
new_parameter = {'kernel':[new_kernel],
'C':update_C['C'],
'gamma':np.logspace(np.log2(new_gamma_grid_interval_left_endpoint),
np.log2(new_gamma_grid_interval_right_endpoint), 11, base=2)}
# 将得到的new_parameter赋值给parameter。
parameter = new_parameter
elif new_kernel == 'linear':
new_C_grid_center = tuned_argument['C'] # 将新计算得到的最优C值作为下一次grid计算的C值的中点。
if new_C_grid_center == np.min(parameter['C']) or new_C_grid_center == np.max(parameter['C']):
print("发现最优参数C为原先的最大/最小值，直接重新设置超参。")
update_C = {'C':np.logspace(np.log10(new_C_grid_center)-5, np.log10(new_C_grid_center)+5, 11)}
else:
new_C_grid_interval_left_endpoint = np.max(parameter['C'][parameter['C']<new_C_grid_center])
new_C_grid_interval_right_endpoint = parameter['C'][-1] + parameter['C'][-1] - parameter['C'][-2]
update_C = {'C':np.logspace(np.log2(new_C_grid_interval_left_endpoint),
np.log2(new_C_grid_interval_right_endpoint), 11, base=2)}
new_parameter = {'kernel': [new_kernel], 'C': update_C['C']}
# 将得到的new_parameter赋值给parameter
parameter = new_parameter
else:
print("其它核函数待补充。")
except (TypeError, SyntaxError):
for i in range(len(parameter)):
#print("现在是parameter的第%d个元素"%i)
if parameter[i]['kernel'] == [new_kernel]: # 只找与新的kernel类型一致的对应参数C和gamma。
if new_kernel == 'rbf':
new_C_grid_center = tuned_argument['C'] # 将新计算得到的最优C值作为下一次grid计算的C值的中点。
new_gamma_grid_center = tuned_argument['gamma'] # 将新计算得到的最优gamma值作为下一次grid计算的gamma值的中点。
if new_C_grid_center == np.min(parameter[i]['C']) or new_C_grid_center == np.max(parameter[i]['C']):
print("发现最优参数C为原先的最大/最小值，直接重新设置超参。")
update_C = {'C':np.logspace(np.log10(new_C_grid_center)-5, np.log10(new_C_grid_center)+5, 11)}
else:
new_C_grid_interval_left_endpoint = np.max(parameter[i]['C'][parameter[i]['C']<new_C_grid_center])
new_C_grid_interval_right_endpoint = np.min(parameter[i]['C'][parameter[i]['C']>new_C_grid_center])
update_C = {'C':np.logspace(np.log2(new_C_grid_interval_left_endpoint),
np.log2(new_C_grid_interval_right_endpoint), 11, base=2)}
if new_gamma_grid_center == np.min(parameter[i]['gamma']) or new_gamma_grid_center == np.max(parameter[i]['gamma']):
print("发现最优参数gamma为原先的最大/最小值，直接重新设置超参。")
new_parameter = {'kernel':[new_kernel],
'C':update_C['C'],
'gamma':np.logspace(np.log10(new_gamma_grid_center)-5, np.log10(new_gamma_grid_center)+5, 11)}
else:
new_gamma_grid_interval_left_endpoint = np.max(parameter[i]['gamma'][parameter[i]['gamma']<new_gamma_grid_center])
new_gamma_grid_interval_right_endpoint = np.min(parameter[i]['gamma'][parameter[i]['gamma']>new_gamma_grid_center])
new_parameter = {'kernel':[new_kernel],
'C':update_C['C'],
'gamma':np.logspace(np.log2(new_gamma_grid_interval_left_endpoint),
np.log2(new_gamma_grid_interval_right_endpoint), 11, base=2)}
# 将得到的new_parameter[i]赋值给parameter[i]
parameter[i] = new_parameter
break
elif new_kernel == 'linear':
new_C_grid_center = tuned_argument['C'] # 将新计算得到的最优C值作为下一次grid计算的C值的中点。
if new_C_grid_center == np.min(parameter[i]['C']) or new_C_grid_center == np.max(parameter[i]['C']):
print("发现最优参数C为原先的最大/最小值，直接重新设置超参。")
new_parameter = {'kernel': [new_kernel],
'C':np.logspace(np.log10(new_C_grid_center)-5, np.log10(new_C_grid_center)+5, 11)}
else:
new_C_grid_interval_left_endpoint = np.max(parameter[i]['C'][parameter[i]['C']<new_C_grid_center])
new_C_grid_interval_right_endpoint = np.min(parameter[i]['C'][parameter[i]['C']>new_C_grid_center])
new_parameter = {'kernel':[new_kernel],
'C': np.logspace(np.log2(new_C_grid_interval_left_endpoint),
np.log2(new_C_grid_interval_right_endpoint), 11, base=2)}
# 将得到的new_parameter[i]赋值给parameter[i]
parameter[i] = new_parameter
break
else:
print("其它核函数待补充。")
# parameter指下一轮微调超参使用的超参范围。tuned_argument指本轮微调得到的最优超参。best_score指本轮得到的最优超参对应的score。
return (parameter, tuned_argument, best_score)
# 从微调好的SVC模型中获取最优超参。
def derive_param_from_tunemodel(iter_best_args):
'''利用tunemodel(X_train, y_train, parameter)计算的结果中第二个元素，即最优参数，提取出来转换成ndarray格式。'''
# 以上一轮获得的新参数C和gamma的1%作为判断参数是否stable的依据。
#print("iter_best_args is", iter_best_args)
if iter_best_args['kernel'] == 'rbf':
new_param = np.array([iter_best_args['C'], iter_best_args['gamma']])
return new_param
elif iter_best_args['kernel'] == 'linear':
new_param = np.array([iter_best_args['C']])
return new_param
else:
return "其它核函数的情况待开发。"
# 迭代调用subtle_tunemodel()，得到最终稳定的SVC模型参数值C和gamma。
def loop_tune(X_train, y_train, class_weight_option, parameter, cv, n=15, threshold=0.05):
'''迭代调用subtle_tunemodel()，得到最终稳定的SVC模型参数值C和gamma。'''
first_parameter = subtle_tunemodel(X_train, y_train, class_weight_option, parameter, cv)
first_parameter_grid_search = first_parameter[0]
first_best_param = derive_param_from_tunemodel(first_parameter[1])
second_parameter = subtle_tunemodel(X_train, y_train, class_weight_option, parameter, cv)
second_parameter_grid_search = second_parameter[0]
second_best_param = derive_param_from_tunemodel(second_parameter[1])
#print("first_best_param is:", first_best_param)
#print("second_best_param is:", second_best_param)
assert isinstance((first_best_param - second_best_param), np.ndarray), "说明是其它核函数，尚未开发相关处理函数。"
delta = np.abs(first_best_param - second_best_param)
old_best_param = second_best_param
crit = old_best_param*threshold
print("循环迭代之前，delta is:", delta)
orgin_count = 0
iter_parameter_grid_search = second_parameter_grid_search
best_score_iter_last = first_parameter[2] # 初始化grid search得到的最优超参对应的score值。
best_score_iter = second_parameter[2]
while np.any(delta > crit):
if (best_score_iter-best_score_iter_last) >= 0:
#print("循环迭代终止条件crit is：", crit)
print("执行tunemodel()函数前，使用的grid search parameter是：\n", iter_parameter_grid_search)
iter_parameter = subtle_tunemodel(X_train, y_train, class_weight_option, parameter, cv)
print("第%d轮loop中，tunemodel函数得到的最优参数是："%orgin_count, iter_parameter)
new_best_param = derive_param_from_tunemodel(iter_parameter[1])
crit = new_best_param * threshold
delta = np.abs(new_best_param - old_best_param)
old_best_param = new_best_param
iter_parameter_grid_search = iter_parameter[0]
best_score_iter_last = best_score_iter
best_score_iter = iter_parameter[2]
print("这是第%d次迭代微调C和gamma。"%orgin_count)
print("第%d次迭代，得到delta："%orgin_count, delta)
orgin_count += 1
if orgin_count>n or np.min(new_best_param)<1e-15 or np.max(new_best_param)>1e+15: # 后一个条件是控制超参微调不要在错误的优化方向浪费算力，超过阈值则跳出。
#pdb.set_trace()
#print("科学记数法比较成功。")
break
else:
raise "超参并没有优化SVC模型，请检查参数。"
try:
iter_best_parameter = iter_parameter[1]
except UnboundLocalError:
iter_best_parameter = second_parameter[1]
return iter_best_parameter
# 画出样本特征数量与AUC数值的关系曲线
def auc_plot_by_feature_selection(feature_count, auc_values):
plt.figure(figsize=(16,9))
plt.xlabel("特征数目")
plt.ylabel("AUC")
plt.title("AUC值与样本特征数目的关系")
plt.plot(feature_count, auc_values)
plt.show()
# 从原样本集中筛选特征子集，这是针对SN或fisher value从大到小排序特征的样本使用的。
def feature_reduce(X, n=3):
return X[:,:-n]
def Mann_Whitney_SumOfRank_test(m, n, m_samples, n_samples, Zcrit=1.96):
'''这是曼.惠特尼 U非参数检验方法。
m和n分别指一对随机向量的容量，为int。m_samples和n_samples指容量分别为m和n的随机向量本身，array_like或df，该df不带label列。
return经曼.惠特尼 U检验筛选的样本集特征名为索引的fisher value，数据类型为Series。'''
if_difference_feature = [] # 用list存储一对随机向量的均值是否有显著差异。1表示有显著差异，0表示没有显著差异。
assert m_samples.shape[1] == n_samples.shape[1], "一对随机向量的列数目应当一致，它们仅样本容量，即行数可能不同"
assert type(m_samples)==pd.DataFrame and type(n_samples)==pd.DataFrame, "一对随机向量的数据结构应为dataframe，便于对随机向量划分标签。"
m_samples["label"], n_samples["label"] = 1, -1 # 给一对随机向量分别加标签，便于后续计算秩和。
total_sample = pd.concat([m_samples, n_samples], ignore_index=True) # reindex，利用其后续计算不同随机向量的秩。
iter_columns = total_sample.columns.drop(["label"])
for column in iter_columns:
sample_mix_sorted_bycolumn = total_sample.loc[:,["label",column]].sort_values(by=[column]) # 对随机向量的某一列升序排序。
rank_bycolumn = sample_mix_sorted_bycolumn[column].rank()
sample_mix_sorted_bycolumn["rank"] = rank_bycolumn
rank_p = sample_mix_sorted_bycolumn[sample_mix_sorted_bycolumn["label"]==1]["rank"] # 抽取label==1的列，得到series。
Wp = rank_p.sum() # 正例样本的秩和
rank_n = sample_mix_sorted_bycolumn[sample_mix_sorted_bycolumn["label"]==-1]["rank"]
Wn = rank_n.sum() # 负例样本的秩和
Up = m*n + m*(m+1)/2 - Wp
Un = m*n + n*(n+1)/2 - Wn
U = np.min([Up, Un])
Z = (U - m*n/2)/math.sqrt(m*n*(m+n+1)/12)
# 当样本为大样本，即正负例样本容量远大于20时，Z近似逼近标准正态分布。我们一般以置信度alpha=0.05来做假设检验。双边检验时，
# Z0.05=+/-1.96。选置信度alpha=0.05.
if abs(Z) > Zcrit:
if_difference_feature.append(True) # 1表示一对随机向量的均值有显著差异。
else:
if_difference_feature.append(False) # 0表示一对随机向量的均值无显著差异。
print(if_difference_feature)
selected_columns = iter_columns[if_difference_feature]
return selected_columns
def multicollinearity_check(sample, coef_corr_crit = 0.7):
'''输入参数是一个不带分类标签label的DataFrame。该DataFrame的全部特征已经经过正负例均值显著性差异的假设检验，即全都是正负例均值
有显著差异的特征向量。因为本函数用于检查多重共线性，当考虑用线性模型时，正负例样本的均值没有显著差异的特征对线性模型没有贡献。
判断两个连续型特征向量是否相关的相关系数阈值，是一个float值，一般定为0.7。
return是一个tuple，包括删除了多重共线性列的新的DataFrame和用list保存的计算得到的各列相关系数结果。'''
corr_list = [] # 用空list存储遍历计算的相关系数，用于后续倒序判断是否删除后一个特征用。
for i in range(sample.columns.size): # i指相关系数对称方阵中的第i行
for j in range(i+1, sample.columns.size): # j指相关系数对称方阵中的第j列
# 第一轮遍历，按照第1列和第2列，第2列和第3列……这样的规律遍历计算相关系数。
#corr_list.append(samples.iloc[:,i].corr(samples.iloc[:,i+1]))
# 判断第i列特征与其它所有列（除第1列之外）的相关系数，如果存在相关系数>相关系数阈值，则说明fishvalue最小的第1列与其它
# 某特征强相关，删除第i列。
corr_list.append(sample.iloc[:,i].corr(sample.iloc[:,j]))
if abs(sample.iloc[:,i].corr(sample.iloc[:,j]))>coef_corr_crit:
sample_independence = sample.drop(labels=sample.columns[i], axis=1)
print("各列相关系数：\n", corr_list)
return (sample_independence, corr_list)
def hist_matrix(df, col_num, bins=100):
'''绘制多个直方图子图。
输入参数df是一个DataFrame，内容为多个特征的样本集。df不应该包括除有意义的特征之外其它列，如label，index等。
输入参数col_num指df的列的数量。绘图的行数row_num由样本特征数目和列数相除向上取整而得。
bins指对特征向量分箱的数目，默认为50。
return一个row_num行，col_num列的的绘图。
'''
import math
# 一共需要绘制多少个子图
sub_fig_num = df.columns.size # 样本特征数目
row_num = math.ceil((sub_fig_num+1)/col_num) # 根据全部指标数目和绘图列数，计算绘图行数。
fig = plt.figure(figsize=(16,9*row_num/col_num))
for i in range(1, sub_fig_num+1):
sub_fig = fig.add_subplot(row_num, col_num, i)
min_by_col = df.iloc[:, i-1].min()
max_by_col = df.iloc[:, i-1].max()
plt.hist(df.iloc[:,i-1], bins=bins, color="steelblue")
plt.xlabel(df.columns[i-1])
plt.ylabel("数值")
plt.title("样本各指标直方图")
plt.subplots_adjust(wspace=9, hspace=16) #调整子图间距
plt.tight_layout() # 调整整图空白。
plt.show()
In [852]:
# 处理训练集、测试集数据，使之能够用于特定模型的训练和测试。主要是删除股票代码，截止日期字段，将数据类型改为ndarray。
# 首先查看训练集和测试集的统计描述，粗略判断mean+/-3*std，与max和min分别比较，检查是否有离群值。
train = train_set.drop(labels=["股票代码", "截止日期", "label"], axis=1)
test = test_set.drop(labels=["股票代码", "截止日期", "label"], axis=1)
stat_result = stat(train)
# 比较样本最大值与mean+3*std，是否>0。
great_than_exist = (stat_result.loc["max"] - (stat_result.loc["mean",:]+3*stat_result.loc["std",:]))>0
# 比较mean-3*std与样本最小值，是否>0。
less_than_exist = (stat_result.loc["min"] - (stat_result.loc["mean",:] - 3*stat_result.loc["std",:]))<0
print("如果>0，说明max值大于mean+3*std，即有离群值：", great_than_exist)
print("\n如果<0，说明min值小于mean-3*std，即有离群值：", less_than_exist)
print("\n以上结果说明训练集中很多特征存在离群值。这预示了单个分类器的性能将不会非常高。")
如果>0，说明max值大于mean+3*std，即有离群值： 0 流动比率 True 速动比率 True 保守速动比率 True 现金比率 True 利息保障倍数B True 资产负债率 True 权益乘数 True 产权比率 True 权益对负债比率 True 有形净值债务率 True 固定资产增长率B True 总资产增长率B True 净资产收益率增长率B True 净利润增长率B True 利润总额增长率B True 营业利润增长率B True 营业收入增长率B True 所有者权益增长率B True 财务杠杆 True 经营杠杆 True 综合杠杆 True 股本总数 True 国有股 True 一般法人配售 True 已流通股份 True 应收账款与收入比 True 应收账款周转率B True 存货与收入比 True 存货周转率B True 应付账款周转率B True 营运资金（资本）周转率B True 现金及现金等价物周转率B True 流动资产与收入比 True 流动资产周转率B True 固定资产与收入比 True 固定资产周转率B True 非流动资产周转率B True 总资产周转率B True 股东权益周转率B True 营业收入现金净含量 True 营业利润现金净含量 True 营运指数 True 资产报酬率B False 总资产净利润率（ROA）B False 净资产收益率B True 营业利润率 True 董事会会议次数 True 监事会会议次数 True 股东大会召开次数 True 董事人数 True 其中：独立董事人数 True 监事总规模 True dtype: bool 如果<0，说明min值小于mean-3*std，即有离群值： 0 流动比率 False 速动比率 False 保守速动比率 False 现金比率 False 利息保障倍数B False 资产负债率 False 权益乘数 False 产权比率 False 权益对负债比率 False 有形净值债务率 False 固定资产增长率B False 总资产增长率B False 净资产收益率增长率B True 净利润增长率B True 利润总额增长率B True 营业利润增长率B False 营业收入增长率B False 所有者权益增长率B True 财务杠杆 False 经营杠杆 True 综合杠杆 True 股本总数 False 国有股 False 一般法人配售 False 已流通股份 False 应收账款与收入比 False 应收账款周转率B False 存货与收入比 False 存货周转率B False 应付账款周转率B False 营运资金（资本）周转率B True 现金及现金等价物周转率B False 流动资产与收入比 False 流动资产周转率B False 固定资产与收入比 False 固定资产周转率B False 非流动资产周转率B False 总资产周转率B False 股东权益周转率B False 营业收入现金净含量 True 营业利润现金净含量 True 营运指数 True 资产报酬率B True 总资产净利润率（ROA）B True 净资产收益率B True 营业利润率 False 董事会会议次数 False 监事会会议次数 False 股东大会召开次数 False 董事人数 True 其中：独立董事人数 True 监事总规模 False dtype: bool 以上结果说明训练集中很多特征存在离群值。这预示了单个分类器的性能将不会非常高。
In [943]:
# 绘制各属性的直方图。
plt_samples = merge_samples.drop(labels=['股票代码', '截止日期', 'label'], axis=1) # 准备绘制用的数据。
hist_matrix(plt_samples, 4)
In [853]:
# 利用fishervalue方法计算的样本特征重要性。
columns_by_fisher = fishvalue(merge_samples.drop(labels=['股票代码', '截止日期'], axis=1))
cols = ['label'] + list(columns_by_fisher.index)
print("按计算的SN或fishervalue排序好的列名（已添加分类标签label）：\n", cols)
print("columns_by_fisher is:\n",columns_by_fisher)
# 按SN信噪比重新排序dataframe的columns顺序。
train_cols_fisher_value_ordered = train.loc[:,cols]
train_cols_fisher_value_ordered['label'] = train_set['label']
test_cols_fisher_value_ordered = test.loc[:,cols]
test_cols_fisher_value_ordered['label'] = test_set['label']
按计算的SN或fishervalue排序好的列名（已添加分类标签label）： ['label', '监事总规模', '权益对负债比率', '非流动资产周转率B', '营业收入现金净含量', '应收账款与收入比', '总资产净利润率（ROA）B', '总资产周转率B', '股东权益周转率B', '应付账款周转率B', '固定资产周转率B', '资产报酬率B', '保守速动比率', '资产负债率', '净资产收益率B', '流动比率', '有形净值债务率', '现金比率', '流动资产周转率B', '现金及现金等价物周转率B', '监事会会议次数', '速动比率', '综合杠杆', '营业利润率', '营业收入增长率B', '财务杠杆', '固定资产与收入比', '存货与收入比', '股本总数', '股东大会召开次数', '已流通股份', '固定资产增长率B', '经营杠杆', '总资产增长率B', '董事人数', '所有者权益增长率B', '其中：独立董事人数', '净利润增长率B', '国有股', '营运资金（资本）周转率B', '营业利润现金净含量', '应收账款周转率B', '利润总额增长率B', '董事会会议次数', '营运指数', '一般法人配售', '产权比率', '权益乘数', '利息保障倍数B', '营业利润增长率B', '净资产收益率增长率B', '存货周转率B', '流动资产与收入比'] columns_by_fisher is: 0 监事总规模 0.548191 权益对负债比率 0.518996 非流动资产周转率B 0.504345 营业收入现金净含量 0.460457 应收账款与收入比 0.445547 总资产净利润率（ROA）B 0.443927 总资产周转率B 0.431815 股东权益周转率B 0.429421 应付账款周转率B 0.415471 固定资产周转率B 0.412982 资产报酬率B 0.406682 保守速动比率 0.398686 资产负债率 0.376918 净资产收益率B 0.350935 流动比率 0.349765 有形净值债务率 0.349547 现金比率 0.340413 流动资产周转率B 0.332929 现金及现金等价物周转率B 0.326825 监事会会议次数 0.253676 速动比率 0.250274 综合杠杆 0.238151 营业利润率 0.232845 营业收入增长率B 0.203102 财务杠杆 0.194951 固定资产与收入比 0.193079 存货与收入比 0.192904 股本总数 0.185301 股东大会召开次数 0.182862 已流通股份 0.178975 固定资产增长率B 0.161007 经营杠杆 0.159151 总资产增长率B 0.156792 董事人数 0.146993 所有者权益增长率B 0.137061 其中：独立董事人数 0.130426 净利润增长率B 0.122319 国有股 0.085387 营运资金（资本）周转率B 0.079066 营业利润现金净含量 0.078418 应收账款周转率B 0.071688 利润总额增长率B 0.064017 董事会会议次数 0.062636 营运指数 0.050425 一般法人配售 0.034427 产权比率 0.028945 权益乘数 0.028945 利息保障倍数B 0.022635 营业利润增长率B 0.020660 净资产收益率增长率B 0.019121 存货周转率B 0.014615 流动资产与收入比 0.003325 dtype: float64
In [854]:
# 其次需要查看样本集中各特征向量之间的相关性，当使用线性模型时，需要考虑多重共线性问题。利用之前按fisher value排序好的特征进行计算。
total_sample = merge_samples.drop(labels=["股票代码", "截止日期", "label"], axis=1)
total_sample = total_sample.loc[:,columns_by_fisher.index]
print("删除了股票代码，截止日期，label三个属性的样本集shape是：", total_sample.shape)
print("检查total_sample的columns：\n", total_sample.columns)
# 将total_sample样本集按label分成正例和负例子集。
p_samples = total_sample[merge_samples['label']==1]
n_samples = total_sample[merge_samples['label']==-1]
p_samples_count = p_samples.shape[0]
n_samples_count = n_samples.shape[0]
feature_selected = Mann_Whitney_SumOfRank_test(p_samples_count, n_samples_count, p_samples, n_samples, Zcrit=1.96)
print("feature_selected的个数：", feature_selected.size)
# 然后对筛选出的正负例样本有显著差异的连续型特征向量计算相关系数，找出高度相关的特征。
feature_test_sample = total_sample.loc[:, feature_selected]
删除了股票代码，截止日期，label三个属性的样本集shape是： (2514, 52)
检查total_sample的columns：
Index(['监事总规模', '权益对负债比率', '非流动资产周转率B', '营业收入现金净含量', '应收账款与收入比',
'总资产净利润率（ROA）B', '总资产周转率B', '股东权益周转率B', '应付账款周转率B', '固定资产周转率B',
'资产报酬率B', '保守速动比率', '资产负债率', '净资产收益率B', '流动比率', '有形净值债务率', '现金比率',
'流动资产周转率B', '现金及现金等价物周转率B', '监事会会议次数', '速动比率', '综合杠杆', '营业利润率',
'营业收入增长率B', '财务杠杆', '固定资产与收入比', '存货与收入比', '股本总数', '股东大会召开次数', '已流通股份',
'固定资产增长率B', '经营杠杆', '总资产增长率B', '董事人数', '所有者权益增长率B', '其中：独立董事人数',
'净利润增长率B', '国有股', '营运资金（资本）周转率B', '营业利润现金净含量', '应收账款周转率B', '利润总额增长率B',
'董事会会议次数', '营运指数', '一般法人配售', '产权比率', '权益乘数', '利息保障倍数B', '营业利润增长率B',
'净资产收益率增长率B', '存货周转率B', '流动资产与收入比'],
dtype='object', name=0)
[True, True, False, True, False, True, False, False, False, False, True, True, True, False, True, True, True, False, True, False, True, True, True, True, True, False, True, True, False, True, True, True, True, False, True, False, True, False, False, True, False, True, False, False, False, False, False, True, False, True, False, False]
feature_selected的个数： 28
In [855]:
# 通过观察训练集的特征属性的统计描述和直方图，我们注意到2个基本情况：
# 1、各特征属性的分布并非正态分布，而是有偏度的（一般右偏，这被认为是符合市场常识的），或者是其它分布。
# 2、各特征属性的数量级不同，且个别特征属性差别非常大，需要标准化。
# 至于训练样本中是否有离群值，这个需要结合具体财报相关知识，以及具体企业在各自行业的规律的业务知识，我们目前无法给出准确判断。从观察
# 看，没有特别夸张的离群数值出现，只有“营业利润增长率”和“利息保障倍数”2个指标有5倍标准差的样本出现（各1例），所以假设训练集中没有
# 离群值，即都是正常业务下的正常范围内数值。
# 对训练集数据进行scaling。我们采用Z-score方法进行scaling。但随后发现“一般法人配售”属性的值在训练集中全为0，但该属性在整个源数据集中
# 并不全为0。我们暂时想保留这个属性，所以放弃Z-score方法，因为该属性的标准差为0.
# ndf_mean = np.mean(ndf, axis=0)
# ndf_std = np.std(ndf, axis=0)
# X = (ndf - ndf_mean) / ndf_std
# scaling思路改为将训练集各属性值映射到[0,1]之间。为方便，直接调用sklearn的preprocessing模块。
# 首先将准备好的训练集、测试集的数据类型从DataFrame改为ndarray。
X_train_orgin = np.array(train_cols_fisher_value_ordered.drop(labels=['label'], axis=1))
y_train = np.array(train_cols_fisher_value_ordered["label"])
X_test_orgin = np.array(test_cols_fisher_value_ordered.drop(labels=['label'], axis=1))
y_test = np.array(test_cols_fisher_value_ordered['label'])
min_max_scaler = preprocessing.MinMaxScaler()
X_train = min_max_scaler.fit_transform(X_train_orgin) # 适用于sklearn svm SVC的训练集。
print("X_train的shape是：", X_train.shape) # 每个属性都在[0,1]之间。至此训练集数据准备就绪。
# 删除数据集的列名，并转换为ndarray格式，然后与训练集做同样的标准化。
X_test = min_max_scaler.fit_transform(X_test_orgin) # 与训练集一样，将test_svm_orgin标准化
print("X_test的shape是：", X_test.shape)
X_train的shape是： (1257, 52) X_test的shape是： (1257, 52)
In [857]:
# SVC训练初始参数。
class_weight_rbf = {-1:15}
class_weight_linear = {-1:30}
class_weight_option = {'rbf':class_weight_rbf, 'linear':class_weight_linear}
#svc = SVC(class_weight=class_weight, random_state=0)#'balanced'
# 针对二元分类，利用AUC进行特征筛选，找到最优特征数目。
auc_values_svc = [] # 用列表存储多次特征筛选后得到的SVC模型的AUC值。
best_params_svc = [] # 用列表存储多次特征筛选后得到的SVC模型的最优参数。
X_train_iter_svc = X_train
X_test_iter_svc = X_test
feature_selection_iteration_count = X_train.shape[1] - 4 # 设置最多要删减多少个属性
reduce_count_per_iterate = 1 # 设置每次删减几个属性
for i in range(feature_selection_iteration_count): # 最多操作10次，删减30个特征。
# 由于tunemodel()函数返回值为parameter，所以将初始化的parameter移到循环内部。
cv_iter = CV_set(10, 0.5, 0.5)
parameter = coarse_tunemodel(X_train_iter_svc, y_train, class_weight_option,
cv_iter, coarse_grid_parameter_boundry=3, coarse_grid_parameter_step_num=3)
print("粗grid search得到的parameter是：\n",parameter)
#pdb.set_trace() # c继续，q中断并离开。
param_op_iter = loop_tune(X_train_iter_svc, y_train, class_weight_option, parameter, cv_iter)
best_params_svc.append(param_op_iter)
# 将舞弊样本权重提高至非舞弊样本权重的20倍后，预测测试集正确率。
train_sample_weight_iter = np.ones(X_train_iter_svc.shape[0], dtype=int)
test_sample_weight_iter = np.ones(X_test_iter_svc.shape[0], dtype=int)
try: # 针对kernel=rbf的情况
clf_op_iter = svc_op(X_train_iter_svc, y_train, class_weight_rbf,
kernel = param_op_iter['kernel'],
C=param_op_iter['C'],
gamma=param_op_iter['gamma'])
train_sample_weight_iter[train_set["label"]==-1] = class_weight_option['rbf'][-1]
# 预测样本的score中，正负例的权重与训练样本中正负例权重保持一致。
test_sample_weight_iter[test_set["label"]==-1] = class_weight_option['rbf'][-1]
except KeyError: # 针对kernel=linear的情况
clf_op_iter = svc_op(X_train_iter_svc, y_train, class_weight_linear, kernel=param_op_iter['kernel'], C=param_op_iter['C'])
train_sample_weight_iter[train_set["label"]==-1] = class_weight_option['linear'][-1]
# 预测样本的score中，正负例的权重与训练样本中正负例权重保持一致。
test_sample_weight_iter[test_set["label"]==-1] = class_weight_option['linear'][-1]
print("SVC模型clf_op_iter的参数是：", clf_op_iter.get_params(deep=True))
# 将舞弊样本权重提高至非舞弊样本权重的20倍后，预测训练集正确率。
print("\n训练模型SVC对训练样本的预测准确率：", clf_op_iter.score(X_train_iter_svc, y_train, sample_weight=train_sample_weight_iter))
y_pred_iter_svc = clf_op_iter.predict(X_test_iter_svc)
print("\n测试集中，预测为舞弊样本的有：", np.where(y_pred_iter_svc==-1),
"\n测试集中，实际为舞弊样本的有：", np.where(y_test==-1),
"\n预测的舞弊样本数目：", y_pred_iter_svc[y_pred_iter_svc==-1].size)
print("\n训练模型SVC对测试样本的预测准确率：", clf_op_iter.score(X_test_iter_svc,y_test, sample_weight=test_sample_weight_iter))
# 计算训练模型clf_op的AUC值，评估模型质量。
auc_value_iter = auc(y_test, y_pred_iter_svc)
auc_values_svc.append(auc_value_iter)
print("以上是第%d次特征筛选。"%i)
print("第%d次特征筛选，AUC值是："%i, auc_value_iter)
print("X_train_iter_svc.shape is:",X_train_iter_svc.shape)
print("X_test_iter_svc.shape is:",X_test_iter_svc.shape)
X_train_iter_svc = feature_reduce(X_train_iter_svc, n=reduce_count_per_iterate)
X_test_iter_svc = feature_reduce(X_test_iter_svc, n=reduce_count_per_iterate)
# 计算特征数目
print("AUC值随特征数目变化：", auc_values_svc)
feature_count_svc = [X_train.shape[1] - reduce_count_per_iterate*i for i in range(feature_selection_iteration_count)]
print("样本特征数目：", feature_count_svc)
auc_plot_by_feature_selection(feature_count_svc, auc_values_svc)
print("各轮特征筛选得到的最优超参是：\n", best_params_svc)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.681 (+/-0.372) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.681 (+/-0.372) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.635 (+/-0.307) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.665 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6397435897435898
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.681 (+/-0.372) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734, 0.64268772, 0.65463617, 0.66680677, 0.67920363,
0.69183097]), 'kernel': ['linear']}], {'C': 0.6309573444801931, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.6309573444801931, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9029187817258884
测试集中，预测为舞弊样本的有： (array([ 370, 658, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 9
训练模型SVC对测试样本的预测准确率： 0.9225888324873096
以上是第0次特征筛选。
第0次特征筛选，AUC值是： 0.8173792499635195
X_train_iter_svc.shape is: (1257, 52)
X_test_iter_svc.shape is: (1257, 52)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.665 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6397435897435898
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734, 0.64268772, 0.65463617, 0.66680677, 0.67920363,
0.69183097]), 'kernel': ['linear']}], {'C': 0.6309573444801931, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.6309573444801931, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9029187817258884
测试集中，预测为舞弊样本的有： (array([ 370, 658, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 9
训练模型SVC对测试样本的预测准确率： 0.9225888324873096
以上是第1次特征筛选。
第1次特征筛选，AUC值是： 0.8173792499635195
X_train_iter_svc.shape is: (1257, 51)
X_test_iter_svc.shape is: (1257, 51)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.330) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.330) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.665 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.330) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734, 0.64268772, 0.65463617, 0.66680677, 0.67920363,
0.69183097]), 'kernel': ['linear']}], {'C': 0.6309573444801931, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.6309573444801931, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9029187817258884
测试集中，预测为舞弊样本的有： (array([ 370, 658, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 9
训练模型SVC对测试样本的预测准确率： 0.9225888324873096
以上是第2次特征筛选。
第2次特征筛选，AUC值是： 0.8173792499635195
X_train_iter_svc.shape is: (1257, 50)
X_test_iter_svc.shape is: (1257, 50)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9022842639593909
测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 5
训练模型SVC对测试样本的预测准确率： 0.8661167512690355
以上是第3次特征筛选。
第3次特征筛选，AUC值是： 0.6814168977090325
X_train_iter_svc.shape is: (1257, 49)
X_test_iter_svc.shape is: (1257, 49)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9022842639593909
测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 5
训练模型SVC对测试样本的预测准确率： 0.8661167512690355
以上是第4次特征筛选。
第4次特征筛选，AUC值是： 0.6814168977090325
X_train_iter_svc.shape is: (1257, 48)
X_test_iter_svc.shape is: (1257, 48)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734, 0.64268772, 0.65463617, 0.66680677, 0.67920363,
0.69183097]), 'kernel': ['linear']}], {'C': 0.6309573444801931, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.6309573444801931, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9029187817258884
测试集中，预测为舞弊样本的有： (array([ 370, 658, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 9
训练模型SVC对测试样本的预测准确率： 0.9225888324873096
以上是第5次特征筛选。
第5次特征筛选，AUC值是： 0.8173792499635195
X_train_iter_svc.shape is: (1257, 47)
X_test_iter_svc.shape is: (1257, 47)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.689 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.687 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.689 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.662 (+/-0.373) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.687 (+/-0.375) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.689 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734, 0.64268772, 0.65463617, 0.66680677, 0.67920363,
0.69183097]), 'kernel': ['linear']}], {'C': 0.6309573444801931, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.6309573444801931, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9029187817258884
测试集中，预测为舞弊样本的有： (array([ 370, 658, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 9
训练模型SVC对测试样本的预测准确率： 0.9225888324873096
以上是第6次特征筛选。
第6次特征筛选，AUC值是： 0.8173792499635195
X_train_iter_svc.shape is: (1257, 46)
X_test_iter_svc.shape is: (1257, 46)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.668 (+/-0.371) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.670 (+/-0.377) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.5754399373371568, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734]), 'kernel': ['linear']}], {'C': 0.5754399373371568, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.05551741]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.5345643593969717, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6665064102564102
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.52674449, 0.52868867, 0.53064002, 0.53259857,
0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
0.54450265]), 'kernel': ['linear']}], {'C': 0.5345643593969717, 'kernel': 'linear'}, 0.6665064102564102)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [0.04087558]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.52674449, 0.52868867, 0.53064002, 0.53259857,
0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
0.54450265]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.5306400191947748, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6665064102564102
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52868867, 0.52907836, 0.52946834, 0.52985862, 0.53024917,
0.53064002, 0.53103115, 0.53142258, 0.53181429, 0.53220629,
0.53259857]), 'kernel': ['linear']}], {'C': 0.5306400191947748, 'kernel': 'linear'}, 0.6665064102564102)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [0.00392434]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.5306400191947748, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9029187817258884
测试集中，预测为舞弊样本的有： (array([ 370, 658, 769, 1246, 1247, 1248, 1251, 1252, 1255, 1256],
dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 10
训练模型SVC对测试样本的预测准确率： 0.9219543147208121
以上是第7次特征筛选。
第7次特征筛选，AUC值是： 0.8169779658543703
X_train_iter_svc.shape is: (1257, 45)
X_test_iter_svc.shape is: (1257, 45)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.668 (+/-0.371) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.670 (+/-0.377) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.5754399373371568, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734]), 'kernel': ['linear']}], {'C': 0.5754399373371568, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.05551741]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.5345643593969717, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6665064102564102
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.52674449, 0.52868867, 0.53064002, 0.53259857,
0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
0.54450265]), 'kernel': ['linear']}], {'C': 0.5345643593969717, 'kernel': 'linear'}, 0.6665064102564102)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [0.04087558]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.52674449, 0.52868867, 0.53064002, 0.53259857,
0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
0.54450265]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.5306400191947748, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6665064102564102
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52868867, 0.52907836, 0.52946834, 0.52985862, 0.53024917,
0.53064002, 0.53103115, 0.53142258, 0.53181429, 0.53220629,
0.53259857]), 'kernel': ['linear']}], {'C': 0.5306400191947748, 'kernel': 'linear'}, 0.6665064102564102)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [0.00392434]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.5306400191947748, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9029187817258884
测试集中，预测为舞弊样本的有： (array([ 370, 658, 769, 1246, 1247, 1248, 1251, 1252, 1255, 1256],
dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 10
训练模型SVC对测试样本的预测准确率： 0.9219543147208121
以上是第8次特征筛选。
第8次特征筛选，AUC值是： 0.8169779658543703
X_train_iter_svc.shape is: (1257, 44)
X_test_iter_svc.shape is: (1257, 44)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.709 (+/-0.417) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.668 (+/-0.371) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.645 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6399038461538461
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.5754399373371568, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734]), 'kernel': ['linear']}], {'C': 0.5754399373371568, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.05551741]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6399038461538461
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.5445026528424212, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6665064102564102
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
0.54450265, 0.54651237, 0.54852951, 0.5505541 , 0.55258616,
0.55462571]), 'kernel': ['linear']}], {'C': 0.5445026528424212, 'kernel': 'linear'}, 0.6665064102564102)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [0.03093728]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
0.54450265, 0.54651237, 0.54852951, 0.5505541 , 0.55258616,
0.55462571]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5365373995198518, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5425003216311503, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5465123745198721, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5485295139412031, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5505540984847945, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5525861556300782, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5546257129579107, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5365373995198518, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5425003216311503, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5465123745198721, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5485295139412031, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5505540984847945, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5525861556300782, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5546257129579107, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6399038461538461
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5365373995198518, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5425003216311503, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5465123745198721, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5485295139412031, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5505540984847945, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5525861556300782, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5546257129579107, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5365373995198518, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5425003216311503, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5465123745198721, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5485295139412031, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5505540984847945, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5525861556300782, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5546257129579107, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.5365373995198518, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6665064102564102
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.53456436, 0.53495839, 0.5353527 , 0.53574731, 0.53614221,
0.5365374 , 0.53693288, 0.53732865, 0.53772472, 0.53812107,
0.53851772]), 'kernel': ['linear']}], {'C': 0.5365373995198518, 'kernel': 'linear'}, 0.6665064102564102)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [0.00796525]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.5365373995198518, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9029187817258884
测试集中，预测为舞弊样本的有： (array([ 370, 658, 769, 1246, 1247, 1248, 1251, 1252, 1255, 1256],
dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 10
训练模型SVC对测试样本的预测准确率： 0.9219543147208121
以上是第9次特征筛选。
第9次特征筛选，AUC值是： 0.8169779658543703
X_train_iter_svc.shape is: (1257, 43)
X_test_iter_svc.shape is: (1257, 43)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.666 (+/-0.372) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.666 (+/-0.372) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9016497461928934
测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 6
训练模型SVC对测试样本的预测准确率： 0.8851522842639594
以上是第10次特征筛选。
第10次特征筛选，AUC值是： 0.726871443163578
X_train_iter_svc.shape is: (1257, 42)
X_test_iter_svc.shape is: (1257, 42)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9016497461928934
测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 6
训练模型SVC对测试样本的预测准确率： 0.8851522842639594
以上是第11次特征筛选。
第11次特征筛选，AUC值是： 0.726871443163578
X_train_iter_svc.shape is: (1257, 41)
X_test_iter_svc.shape is: (1257, 41)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9016497461928934
测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 6
训练模型SVC对测试样本的预测准确率： 0.8851522842639594
以上是第12次特征筛选。
第12次特征筛选，AUC值是： 0.726871443163578
X_train_iter_svc.shape is: (1257, 40)
X_test_iter_svc.shape is: (1257, 40)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9016497461928934
测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 6
训练模型SVC对测试样本的预测准确率： 0.8851522842639594
以上是第13次特征筛选。
第13次特征筛选，AUC值是： 0.726871443163578
X_train_iter_svc.shape is: (1257, 39)
X_test_iter_svc.shape is: (1257, 39)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9016497461928934
测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 6
训练模型SVC对测试样本的预测准确率： 0.8851522842639594
以上是第14次特征筛选。
第14次特征筛选，AUC值是： 0.726871443163578
X_train_iter_svc.shape is: (1257, 38)
X_test_iter_svc.shape is: (1257, 38)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9016497461928934
测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 6
训练模型SVC对测试样本的预测准确率： 0.8851522842639594
以上是第15次特征筛选。
第15次特征筛选，AUC值是： 0.726871443163578
X_train_iter_svc.shape is: (1257, 37)
X_test_iter_svc.shape is: (1257, 37)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.722 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.641 (+/-0.235) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6411858974358975
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6411858974358975
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.672 (+/-0.377) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.377) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.381) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6658653846153846
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.437) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.436) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.437) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.689 (+/-0.436) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.722 (+/-0.473) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.697 (+/-0.437) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.436) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.645 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.722 (+/-0.473) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.697 (+/-0.437) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.388) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.388) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.689 (+/-0.436) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.388) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.647 (+/-0.387) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.722 (+/-0.473) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.697 (+/-0.437) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.388) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.689 (+/-0.436) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.689 (+/-0.436) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.689 (+/-0.436) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.647 (+/-0.387) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.664 (+/-0.388) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.689 (+/-0.436) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.689 (+/-0.436) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.689 (+/-0.436) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.647 (+/-0.387) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.646 (+/-0.388) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.656 (+/-0.384) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.645 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.615 (+/-0.239) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.616 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.615 (+/-0.239) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.589 (+/-0.232) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.616 (+/-0.237) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.616 (+/-0.237) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.237) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.615 (+/-0.239) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.613 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.589 (+/-0.232) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.616 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.616 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.616 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.615 (+/-0.239) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.588 (+/-0.232) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.616 (+/-0.237) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.616 (+/-0.237) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.616 (+/-0.237) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.616 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.615 (+/-0.240) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.589 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.616 (+/-0.237) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.616 (+/-0.237) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.616 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.615 (+/-0.240) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.614 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.239) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.589 (+/-0.232) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.240) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.615 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6387820512820513
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.674 (+/-0.375) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.681 (+/-0.372) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.689 (+/-0.374) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.681 (+/-0.372) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.641 (+/-0.307) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.635 (+/-0.308) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.619 (+/-0.321) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.681 (+/-0.372) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.681 (+/-0.372) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.306) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.644 (+/-0.390) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.647 (+/-0.387) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.681 (+/-0.372) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.681 (+/-0.372) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.645 (+/-0.306) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.648 (+/-0.386) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.646 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.449) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.681 (+/-0.372) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.383) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.681 (+/-0.372) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.645 (+/-0.306) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.654 (+/-0.379) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.652 (+/-0.385) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.645 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.681 (+/-0.372) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.681 (+/-0.372) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.645 (+/-0.306) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.653 (+/-0.379) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.649 (+/-0.386) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.644 (+/-0.389) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.449) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.681 (+/-0.372) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.670 (+/-0.370) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.653 (+/-0.379) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.657 (+/-0.376) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.644 (+/-0.389) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.681 (+/-0.372) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.681 (+/-0.372) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.371) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.653 (+/-0.379) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.657 (+/-0.376) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.653 (+/-0.379) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.646 (+/-0.388) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.656 (+/-0.384) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.371) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.653 (+/-0.379) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.656 (+/-0.377) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.651 (+/-0.381) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.645 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.640 (+/-0.234) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.665 (+/-0.313) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.313) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.235) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.613 (+/-0.240) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.641 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.314) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.313) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.313) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.636 (+/-0.325) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.613 (+/-0.240) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.588 (+/-0.233) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.641 (+/-0.236) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.314) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.313) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.313) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.661 (+/-0.314) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.637 (+/-0.325) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.613 (+/-0.239) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.589 (+/-0.232) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.314) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.313) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.313) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.660 (+/-0.313) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.637 (+/-0.324) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.613 (+/-0.239) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.588 (+/-0.232) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.641 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.313) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.313) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.660 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.662 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.612 (+/-0.239) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.589 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.313) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.660 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.662 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.637 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.239) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.589 (+/-0.232) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.240) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.664 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.661 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.637 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6658653846153846
循环迭代之前，delta is: [6.01892829e+01 1.51188643e-02]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([25.11886432, 27.54228703, 30.1995172 , 33.11311215, 36.30780548,
39.81071706, 43.65158322, 47.86300923, 52.48074602, 57.54399373,
63.09573445]), 'kernel': ['rbf'], 'gamma': array([0.01584893, 0.01737801, 0.01905461, 0.02089296, 0.02290868,
0.02511886, 0.02754229, 0.03019952, 0.03311311, 0.03630781,
0.03981072])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.722 (+/-0.473) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.747 (+/-0.502) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.722 (+/-0.473) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.689 (+/-0.436) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.689 (+/-0.436) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.689 (+/-0.436) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.689 (+/-0.436) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.689 (+/-0.436) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.689 (+/-0.436) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.689 (+/-0.436) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.388) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.689 (+/-0.436) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.689 (+/-0.436) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.664 (+/-0.388) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.388) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.689 (+/-0.436) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.689 (+/-0.436) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.664 (+/-0.388) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.664 (+/-0.388) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.388) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.689 (+/-0.436) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.689 (+/-0.436) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.664 (+/-0.388) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.664 (+/-0.388) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.388) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.689 (+/-0.436) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.689 (+/-0.436) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.617 (+/-0.238) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.617 (+/-0.238) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.747 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.714 (+/-0.418) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.681 (+/-0.372) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.681 (+/-0.372) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.372) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.689 (+/-0.374) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.689 (+/-0.374) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.449) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.689 (+/-0.374) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.681 (+/-0.372) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.681 (+/-0.372) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.681 (+/-0.372) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.664 (+/-0.388) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.689 (+/-0.374) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.689 (+/-0.374) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.698 (+/-0.383) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.449) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.681 (+/-0.372) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.681 (+/-0.372) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.681 (+/-0.372) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.681 (+/-0.372) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.698 (+/-0.383) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.698 (+/-0.383) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.698 (+/-0.383) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.698 (+/-0.383) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.374) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.382) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.681 (+/-0.372) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.681 (+/-0.372) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.681 (+/-0.372) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.664 (+/-0.388) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.698 (+/-0.383) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.698 (+/-0.383) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.698 (+/-0.383) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.698 (+/-0.383) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.382) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.681 (+/-0.372) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.681 (+/-0.372) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.681 (+/-0.372) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.681 (+/-0.372) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.698 (+/-0.383) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.698 (+/-0.383) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.698 (+/-0.383) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.698 (+/-0.383) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.684 (+/-0.372) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.689 (+/-0.374) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.684 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.681 (+/-0.372) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.689 (+/-0.374) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.698 (+/-0.383) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.698 (+/-0.383) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.698 (+/-0.383) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.698 (+/-0.383) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.684 (+/-0.372) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.681 (+/-0.372) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.681 (+/-0.372) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.374) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.698 (+/-0.383) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.698 (+/-0.383) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.698 (+/-0.383) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.698 (+/-0.383) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.684 (+/-0.372) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.681 (+/-0.372) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.681 (+/-0.372) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.656 (+/-0.312) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.374) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.698 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.698 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.698 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.698 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.684 (+/-0.372) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.681 (+/-0.372) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.681 (+/-0.372) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.656 (+/-0.312) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.648 (+/-0.306) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.698 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.698 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.698 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.698 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.684 (+/-0.372) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.372) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.681 (+/-0.372) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.656 (+/-0.312) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.648 (+/-0.306) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.643 (+/-0.306) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.684 (+/-0.372) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.681 (+/-0.372) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.372) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.656 (+/-0.312) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.648 (+/-0.306) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.648 (+/-0.306) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.641 (+/-0.306) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.236) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.666 (+/-0.315) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.236) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.235) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.235) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.641 (+/-0.235) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.641 (+/-0.235) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.641 (+/-0.235) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.666 (+/-0.315) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.666 (+/-0.315) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.666 (+/-0.315) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.666 (+/-0.315) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.235) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.235) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.641 (+/-0.235) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.641 (+/-0.235) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.314) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.235) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.641 (+/-0.235) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.641 (+/-0.235) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.665 (+/-0.314) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.235) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.235) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.665 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.665 (+/-0.313) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.235) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.314) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.666 (+/-0.314) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.315) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.666 (+/-0.315) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.666 (+/-0.315) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.666 (+/-0.315) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.665 (+/-0.314) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.665 (+/-0.313) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.665 (+/-0.313) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.314) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.314) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.666 (+/-0.315) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.315) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.666 (+/-0.315) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.666 (+/-0.315) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.665 (+/-0.314) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.665 (+/-0.313) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.665 (+/-0.313) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.665 (+/-0.313) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.314) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.314) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.665 (+/-0.313) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.665 (+/-0.313) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.665 (+/-0.313) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.665 (+/-0.314) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.665 (+/-0.314) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.313) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.665 (+/-0.313) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.665 (+/-0.313) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.665 (+/-0.314) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.314) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.665 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.665 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6658653846153846
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([25.11886432, 25.58585887, 26.0615355 , 26.54605562, 27.03958364,
27.54228703, 28.05433638, 28.57590543, 29.10717118, 29.6483139 ,
30.1995172 ]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-07, 3.98107171e-06, 3.98107171e-05, 3.98107171e-04,
3.98107171e-03, 3.98107171e-02, 3.98107171e-01, 3.98107171e+00,
3.98107171e+01, 3.98107171e+02, 3.98107171e+03])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}, 0.6658653846153846)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [12.26843002 0.01469185]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([25.11886432, 25.58585887, 26.0615355 , 26.54605562, 27.03958364,
27.54228703, 28.05433638, 28.57590543, 29.10717118, 29.6483139 ,
30.1995172 ]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-07, 3.98107171e-06, 3.98107171e-05, 3.98107171e-04,
3.98107171e-03, 3.98107171e-02, 3.98107171e-01, 3.98107171e+00,
3.98107171e+01, 3.98107171e+02, 3.98107171e+03])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.613 (+/-0.240) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.613 (+/-0.240) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.240) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.240) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.689 (+/-0.374) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.689 (+/-0.374) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.689 (+/-0.374) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.689 (+/-0.374) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.613 (+/-0.240) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.641 (+/-0.235) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.613 (+/-0.240) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.641 (+/-0.235) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.613 (+/-0.240) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.641 (+/-0.235) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.240) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.240) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6658653846153846
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([26.54605562, 26.64403528, 26.74237657, 26.84108084, 26.94014941,
27.03958364, 27.13938488, 27.23955447, 27.34009379, 27.44100418,
27.54228703]), 'kernel': ['rbf'], 'gamma': array([0.00398107, 0.00630957, 0.01 , 0.01584893, 0.02511886,
0.03981072, 0.06309573, 0.1 , 0.15848932, 0.25118864,
0.39810717])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}, 0.6658653846153846)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [5.02703392e-01 4.16333634e-17]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 0.03981071705534969, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 27.039583641088424, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9468462083628633
测试集中，预测为舞弊样本的有： (array([ 370, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 8
训练模型SVC对测试样本的预测准确率： 0.9567682494684621
以上是第16次特征筛选。
第16次特征筛选，AUC值是： 0.8177805340726688
X_train_iter_svc.shape is: (1257, 36)
X_test_iter_svc.shape is: (1257, 36)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.674 (+/-0.375) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 10000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.12 0.17 0.14 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6392628205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.654 (+/-0.393) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.381) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 10000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.318) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.674 (+/-0.375) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.12 0.17 0.14 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6392628205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.654 (+/-0.393) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.381) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.662 (+/-0.373) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.659 (+/-0.318) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9016497461928934
测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 6
训练模型SVC对测试样本的预测准确率： 0.8851522842639594
以上是第17次特征筛选。
第17次特征筛选，AUC值是： 0.726871443163578
X_train_iter_svc.shape is: (1257, 35)
X_test_iter_svc.shape is: (1257, 35)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.674 (+/-0.375) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 10000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.12 0.17 0.14 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6392628205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.654 (+/-0.393) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.381) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 10000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.318) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.674 (+/-0.375) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.12 0.17 0.14 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6392628205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.654 (+/-0.393) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.381) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.662 (+/-0.373) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.659 (+/-0.318) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9016497461928934
测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 5
训练模型SVC对测试样本的预测准确率： 0.8661167512690355
以上是第18次特征筛选。
第18次特征筛选，AUC值是： 0.6814168977090325
X_train_iter_svc.shape is: (1257, 34)
X_test_iter_svc.shape is: (1257, 34)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6375
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.654 (+/-0.393) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6373397435897435
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.654 (+/-0.393) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.723 (+/-0.423) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.666 (+/-0.371) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9016497461928934
测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 5
训练模型SVC对测试样本的预测准确率： 0.8661167512690355
以上是第19次特征筛选。
第19次特征筛选，AUC值是： 0.6814168977090325
X_train_iter_svc.shape is: (1257, 33)
X_test_iter_svc.shape is: (1257, 33)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6375
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.654 (+/-0.393) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6373397435897435
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.654 (+/-0.393) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.666 (+/-0.371) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9016497461928934
测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 5
训练模型SVC对测试样本的预测准确率： 0.8661167512690355
以上是第20次特征筛选。
第20次特征筛选，AUC值是： 0.6814168977090325
X_train_iter_svc.shape is: (1257, 32)
X_test_iter_svc.shape is: (1257, 32)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6375
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6373397435897435
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.706 (+/-0.418) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.666 (+/-0.371) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9016497461928934
测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 5
训练模型SVC对测试样本的预测准确率： 0.8661167512690355
以上是第21次特征筛选。
第21次特征筛选，AUC值是： 0.6814168977090325
X_train_iter_svc.shape is: (1257, 31)
X_test_iter_svc.shape is: (1257, 31)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6375
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.384) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6371794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.706 (+/-0.418) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.666 (+/-0.371) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.384) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9016497461928934
测试集中，预测为舞弊样本的有： (array([1248, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 4
训练模型SVC对测试样本的预测准确率： 0.866751269035533
以上是第22次特征筛选。
第22次特征筛选，AUC值是： 0.6818181818181819
X_train_iter_svc.shape is: (1257, 30)
X_test_iter_svc.shape is: (1257, 30)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.98 0.99 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6375
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.654 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.384) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6371794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.654 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.706 (+/-0.418) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.670 (+/-0.370) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.384) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9016497461928934
测试集中，预测为舞弊样本的有： (array([1248, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 4
训练模型SVC对测试样本的预测准确率： 0.866751269035533
以上是第23次特征筛选。
第23次特征筛选，AUC值是： 0.6818181818181819
X_train_iter_svc.shape is: (1257, 29)
X_test_iter_svc.shape is: (1257, 29)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.631 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.672 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.706 (+/-0.424) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.706 (+/-0.424) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.424) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 100.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 1000.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10000.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.638 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6399038461538461
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.328) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.385) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 100.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 1000.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10000.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.638 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.672 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.706 (+/-0.424) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.706 (+/-0.424) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.424) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.638 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6399038461538461
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.328) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.706 (+/-0.418) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.385) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.638 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9016497461928934
测试集中，预测为舞弊样本的有： (array([1248, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 4
训练模型SVC对测试样本的预测准确率： 0.866751269035533
以上是第24次特征筛选。
第24次特征筛选，AUC值是： 0.6818181818181819
X_train_iter_svc.shape is: (1257, 28)
X_test_iter_svc.shape is: (1257, 28)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.672 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6381410256410257
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.653 (+/-0.394) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.652 (+/-0.395) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.652 (+/-0.395) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.672 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.653 (+/-0.394) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.652 (+/-0.395) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.652 (+/-0.395) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.706 (+/-0.418) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.313) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9022842639593909
测试集中，预测为舞弊样本的有： (array([1248, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 4
训练模型SVC对测试样本的预测准确率： 0.866751269035533
以上是第25次特征筛选。
第25次特征筛选，AUC值是： 0.6818181818181819
X_train_iter_svc.shape is: (1257, 27)
X_test_iter_svc.shape is: (1257, 27)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6413461538461539
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.672 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.710 (+/-0.420) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.798 (+/-0.437) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.664 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.631 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.353) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.632 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.675 (+/-0.375) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.615 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.612 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.328) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.653 (+/-0.394) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.357) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.392) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.631 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.367) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.632 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.648 (+/-0.778) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.684 (+/-0.426) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.627 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.665 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.591 (+/-0.324) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.665 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.612 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.325) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6799669181301015
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.722 (+/-0.473) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.437) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.772 (+/-0.473) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.437) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.423) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.739 (+/-0.452) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.423) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.473) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.449) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.723 (+/-0.417) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.739 (+/-0.452) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.722 (+/-0.417) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.449) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.731 (+/-0.422) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.689 (+/-0.436) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.739 (+/-0.452) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.714 (+/-0.418) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.731 (+/-0.422) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.698 (+/-0.383) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.388) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.702 (+/-0.421) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.718 (+/-0.416) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.653 (+/-0.394) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.772 (+/-0.473) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.781 (+/-0.417) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.723 (+/-0.417) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.731 (+/-0.422) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.710 (+/-0.420) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.620 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.810 (+/-0.465) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.710 (+/-0.420) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.354) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.687 (+/-0.351) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.672 (+/-0.392) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.635 (+/-0.326) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.683 (+/-0.378) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.629 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.619 (+/-0.321) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.758 (+/-0.484) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.707 (+/-0.423) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.355) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.693 (+/-0.350) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.401) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.630 (+/-0.320) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.668 (+/-0.379) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.619 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.755 (+/-0.488) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.671 (+/-0.378) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.639 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.695 (+/-0.353) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.347) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.627 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.629 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.632 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.616 (+/-0.237) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.616 (+/-0.237) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.641 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.641 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.616 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.615 (+/-0.239) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.612 (+/-0.242) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.641 (+/-0.235) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.326) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.640 (+/-0.324) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.641 (+/-0.235) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.294) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.326) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.639 (+/-0.324) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.636 (+/-0.325) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.640 (+/-0.234) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.655 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.638 (+/-0.325) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.636 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.636 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.739 (+/-0.398) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.698 (+/-0.383) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.798 (+/-0.437) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.723 (+/-0.417) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.698 (+/-0.383) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.418) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.391) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.395) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.723 (+/-0.423) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.419) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.395) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.398) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.764 (+/-0.421) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.706 (+/-0.418) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.381) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.848 (+/-0.460) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.773 (+/-0.416) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.739 (+/-0.391) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.395) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.391) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.702 (+/-0.420) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.378) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.386) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.823 (+/-0.451) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.699 (+/-0.358) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.678 (+/-0.357) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.374) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.372) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.667 (+/-0.379) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.625 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.655 (+/-0.392) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.772 (+/-0.473) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.735 (+/-0.455) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.669 (+/-0.373) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.649 (+/-0.364) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.687 (+/-0.358) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.621 (+/-0.299) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.385) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.626 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.395) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.764 (+/-0.478) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.733 (+/-0.458) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.686 (+/-0.433) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.663 (+/-0.367) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.654 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.659 (+/-0.386) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.386) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.620 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.418) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.720 (+/-0.464) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.671 (+/-0.436) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.668 (+/-0.363) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.647 (+/-0.288) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.632 (+/-0.324) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.620 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.385) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.629 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.619 (+/-0.321) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.672 (+/-0.371) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.773 (+/-0.474) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.711 (+/-0.472) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.671 (+/-0.434) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.645 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.390) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.620 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.384) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.619 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.367) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.748 (+/-0.449) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.669 (+/-0.395) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.679 (+/-0.430) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.648 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.648 (+/-0.297) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.630 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.620 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.628 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.632 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.295) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.683 (+/-0.296) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.313) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.294) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.682 (+/-0.295) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.314) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.295) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.682 (+/-0.295) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.314) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.295) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.682 (+/-0.295) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.313) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.314) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.312) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.295) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.636 (+/-0.323) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.665 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.241) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.664 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.659 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.235) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.288) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.679 (+/-0.290) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.317) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.665 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.678 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.654 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.636 (+/-0.325) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.665 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.655 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.664 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.676 (+/-0.288) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.653 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.636 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.636 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.325) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
循环迭代之前，delta is: [3.69042656e+05 7.48811357e-07]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([ 398107.1705535 , 436515.83224017, 478630.09232264,
524807.46024977, 575439.93733716, 630957.34448019,
691830.97091894, 758577.57502918, 831763.77110267,
912010.83935591, 1000000. ]), 'kernel': ['rbf'], 'gamma': array([1.58489319e-07, 1.73780083e-07, 1.90546072e-07, 2.08929613e-07,
2.29086765e-07, 2.51188643e-07, 2.75422870e-07, 3.01995172e-07,
3.31131121e-07, 3.63078055e-07, 3.98107171e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6666666666666665
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.823 (+/-0.451) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.848 (+/-0.460) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.823 (+/-0.451) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.459) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.748 (+/-0.449) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.823 (+/-0.451) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.459) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.748 (+/-0.449) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.848 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.823 (+/-0.451) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.683 (+/-0.296) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.658 (+/-0.217) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.666 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.658 (+/-0.217) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.666 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.325) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([436515.83224017, 444631.26746911, 452897.57990362, 461317.57456038,
469894.10860521, 478630.09232264, 487528.49010339, 496592.32145034,
505824.66200311, 515228.64458176, 524807.46024977]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-07, 2.33345806e-07, 2.37684029e-07, 2.42102905e-07,
2.46603934e-07, 2.51188643e-07, 2.55858589e-07, 2.60615355e-07,
2.65460556e-07, 2.70395836e-07, 2.75422870e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}, 0.6833333333333332)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [1.52327252e+05 6.35274710e-22]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([436515.83224017, 444631.26746911, 452897.57990362, 461317.57456038,
469894.10860521, 478630.09232264, 487528.49010339, 496592.32145034,
505824.66200311, 515228.64458176, 524807.46024977]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-07, 2.33345806e-07, 2.37684029e-07, 2.42102905e-07,
2.46603934e-07, 2.51188643e-07, 2.55858589e-07, 2.60615355e-07,
2.65460556e-07, 2.70395836e-07, 2.75422870e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6416666666666666
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.325) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([436515.83224017, 438126.98202249, 439744.07844737, 441367.14346344,
442996.19910036, 444631.26746911, 446272.37076225, 447919.53125428,
449572.77130191, 451232.11334434, 452897.57990362]), 'kernel': ['rbf'], 'gamma': array([2.46603934e-07, 2.47514132e-07, 2.48427689e-07, 2.49344619e-07,
2.50264933e-07, 2.51188643e-07, 2.52115763e-07, 2.53046305e-07,
2.53980281e-07, 2.54917705e-07, 2.55858589e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}, 0.6833333333333332)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [33998.82485353 0. ]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([436515.83224017, 438126.98202249, 439744.07844737, 441367.14346344,
442996.19910036, 444631.26746911, 446272.37076225, 447919.53125428,
449572.77130191, 451232.11334434, 452897.57990362]), 'kernel': ['rbf'], 'gamma': array([2.46603934e-07, 2.47514132e-07, 2.48427689e-07, 2.49344619e-07,
2.50264933e-07, 2.51188643e-07, 2.52115763e-07, 2.53046305e-07,
2.53980281e-07, 2.54917705e-07, 2.55858589e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6416666666666666
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.798 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.798 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.667 (+/-0.316) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.667 (+/-0.316) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.325) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
发现最优参数C为原先的最大/最小值，直接重新设置超参。
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([4.36515832e+00, 4.36515832e+01, 4.36515832e+02, 4.36515832e+03,
4.36515832e+04, 4.36515832e+05, 4.36515832e+06, 4.36515832e+07,
4.36515832e+08, 4.36515832e+09, 4.36515832e+10]), 'kernel': ['rbf'], 'gamma': array([2.49344619e-07, 2.49528410e-07, 2.49712337e-07, 2.49896400e-07,
2.50080598e-07, 2.50264933e-07, 2.50449403e-07, 2.50634008e-07,
2.50818751e-07, 2.51003629e-07, 2.51188643e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}, 0.6833333333333332)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [8.11543523e+03 9.23710578e-10]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 2.50264932573108e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 436515.83224016585, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9362154500354358
测试集中，预测为舞弊样本的有： (array([ 370, 658, 1246, 1247, 1248, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 7
训练模型SVC对测试样本的预测准确率： 0.9347980155917789
以上是第26次特征筛选。
第26次特征筛选，AUC值是： 0.7264701590544287
X_train_iter_svc.shape is: (1257, 26)
X_test_iter_svc.shape is: (1257, 26)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.748 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.666 (+/-0.316) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6663461538461538
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.748 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.706 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.798 (+/-0.437) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.685 (+/-0.376) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.631 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.316) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.641 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.08 0.17 0.11 6
1 0.99 0.98 0.99 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6868579437711271
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.644 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.723 (+/-0.358) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.661 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.723 (+/-0.358) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.385) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.702 (+/-0.355) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.654 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.386) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.372) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.598 (+/-0.745) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.367) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.242) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.682 (+/-0.294) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.290) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.561 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.640 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.657 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.07 0.17 0.10 6
1 0.99 0.98 0.99 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6868579437711271
发现最优参数C为原先的最大/最小值，直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.772 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.772 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.747 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.798 (+/-0.437) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.417) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.689 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.698 (+/-0.383) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.375) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.685 (+/-0.376) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.683 (+/-0.378) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.644 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.644 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.676 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.438) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.660 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.629 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.631 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.644 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.624 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.639 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.377) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.634 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.376) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.376) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.376) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.376) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.376) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.642 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.235) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.664 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.664 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.681 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.639 (+/-0.329) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.687 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.688 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.317) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.634 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.638 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.320) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.317) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.320) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.317) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.320) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.317) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.320) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.317) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.320) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.317) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.11 0.17 0.13 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6879802539368455
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.797 (+/-0.492) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.723 (+/-0.358) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.702 (+/-0.355) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.673 (+/-0.330) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.676 (+/-0.370) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.669 (+/-0.373) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.665 (+/-0.381) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.654 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.330) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.375) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.627 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.386) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.385) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.632 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.644 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.642 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.390) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.622 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.629 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.644 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.624 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.639 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.634 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.642 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.682 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.294) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.665 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.680 (+/-0.290) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.664 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.676 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.654 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.657 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.687 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.688 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.317) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.634 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.638 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.312) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.313) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.314) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.312) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.312) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.312) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.314) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.314) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.312) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.314) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.314) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.11 0.17 0.13 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6879802539368455
循环迭代之前，delta is: [9.00000000e+07 1.51188643e-05]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([ 1000000. , 1584893.19246111, 2511886.43150958,
3981071.70553497, 6309573.44480193, 9999999.99999999,
15848931.92461113, 25118864.31509581, 39810717.05534975,
63095734.44801924, 99999999.99999991]), 'kernel': ['rbf'], 'gamma': array([1.58489319e-05, 1.73780083e-05, 1.90546072e-05, 2.08929613e-05,
2.29086765e-05, 2.51188643e-05, 2.75422870e-05, 3.01995172e-05,
3.31131121e-05, 3.63078055e-05, 3.98107171e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.683 (+/-0.378) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.691 (+/-0.387) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.658 (+/-0.329) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.660 (+/-0.327) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.658 (+/-0.329) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.644 (+/-0.319) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.654 (+/-0.332) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.670 (+/-0.381) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.646 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.646 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.330) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.654 (+/-0.332) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.643 (+/-0.320) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.646 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.649 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.636 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.638 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.643 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.639 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.644 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.635 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.633 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.641 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.649 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.636 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.649 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.637 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.641 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.634 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.631 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.635 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.646 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.674 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.639 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.648 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.641 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.643 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.633 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.634 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.637 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.641 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.646 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.674 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.662 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.641 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.644 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.648 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.633 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.625 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.626 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.652 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.649 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.624 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.649 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.637 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.628 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.669 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.652 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.645 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.624 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.627 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.643 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.631 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.669 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.656 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.647 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.634 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.622 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.656 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.321) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.647 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.631 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.321) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.641 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.634 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.624 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.626 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.641 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.649 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.664 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.664 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.664 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.663 (+/-0.318) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.318) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.319) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.318) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.662 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.318) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.318) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.663 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.663 (+/-0.318) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.662 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.237) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.688 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.686 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.688 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.224) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.687 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.664 (+/-0.223) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.614 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.326) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.241) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.689 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.224) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.639 (+/-0.236) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.614 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.637 (+/-0.326) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.638 (+/-0.240) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.241) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.688 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.613 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.242) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.326) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.237) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.612 (+/-0.243) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.239) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.326) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.612 (+/-0.242) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.238) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.11 0.17 0.13 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6886212795778712
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.660 (+/-0.385) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.665 (+/-0.382) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.626 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.627 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.633 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.632 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.631 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.632 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.641 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.634 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.634 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.635 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.626 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.631 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.639 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.632 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.630 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.636 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.638 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.643 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.637 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.641 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.629 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.633 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.637 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.649 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.636 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.649 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.637 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.641 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.634 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.631 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.635 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.646 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.674 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.639 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.648 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.641 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.643 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.633 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.634 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.637 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.641 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.646 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.674 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.662 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.641 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.644 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.648 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.633 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.625 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.626 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.652 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.649 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.624 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.649 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.637 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.628 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.669 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.652 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.645 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.624 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.627 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.643 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.631 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.669 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.656 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.647 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.634 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.622 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.656 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.321) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.647 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.631 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.321) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.641 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.634 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.624 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.626 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.641 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.649 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.660 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.659 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.660 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.661 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.661 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.662 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.662 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.662 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.660 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.661 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.662 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.662 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.661 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.237) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.688 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.686 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.688 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.224) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.687 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.664 (+/-0.223) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.614 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.326) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.241) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.689 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.224) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.639 (+/-0.236) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.614 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.637 (+/-0.326) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.638 (+/-0.240) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.241) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.688 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.613 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.242) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.326) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.237) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.612 (+/-0.243) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.239) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.326) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.612 (+/-0.242) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.238) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.11 0.17 0.13 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6886212795778712
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([ 9999999.99999999, 10964781.96143185, 12022644.34617414,
13182567.38556406, 14454397.70745927, 15848931.92461113,
17378008.28749376, 19054607.17963249, 20892961.30854038,
22908676.52767773, 25118864.31509581]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-05, 2.33345806e-05, 2.37684029e-05, 2.42102905e-05,
2.46603934e-05, 2.51188643e-05, 2.55858589e-05, 2.60615355e-05,
2.65460556e-05, 2.70395836e-05, 2.75422870e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}, 0.6886212795778712)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [5848931.92461113 0. ]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([ 9999999.99999999, 10964781.96143185, 12022644.34617414,
13182567.38556406, 14454397.70745927, 15848931.92461113,
17378008.28749376, 19054607.17963249, 20892961.30854038,
22908676.52767773, 25118864.31509581]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-05, 2.33345806e-05, 2.37684029e-05, 2.42102905e-05,
2.46603934e-05, 2.51188643e-05, 2.55858589e-05, 2.60615355e-05,
2.65460556e-05, 2.70395836e-05, 2.75422870e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.626 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.326) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.641 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.664 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.656 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.634 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.297) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.645 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.655 (+/-0.292) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.658 (+/-0.292) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.634 (+/-0.321) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.668 (+/-0.295) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.645 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.658 (+/-0.292) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.635 (+/-0.308) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.658 (+/-0.292) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.643 (+/-0.310) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.666 (+/-0.297) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.641 (+/-0.307) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.632 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.643 (+/-0.306) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.656 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.647 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.624 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.666 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.643 (+/-0.306) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.631 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.624 (+/-0.319) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.667 (+/-0.301) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.641 (+/-0.307) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.630 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.668 (+/-0.300) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.698 (+/-0.352) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.643 (+/-0.310) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.706 (+/-0.353) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.652 (+/-0.313) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.639 (+/-0.309) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.698 (+/-0.352) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.683 (+/-0.314) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.627 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.638 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.681 (+/-0.372) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.613 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.662 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.224) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.240) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.317) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.297) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.327) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.323) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.663 (+/-0.317) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.637 (+/-0.324) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.238) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.236) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.637 (+/-0.324) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.689 (+/-0.298) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.639 (+/-0.238) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.236) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.689 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.237) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.240) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.662 (+/-0.315) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.315) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.665 (+/-0.225) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.239) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.235) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.239) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.689 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.239) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.662 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.239) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.639 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.10 0.17 0.12 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.688941792398384
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.626 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.326) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.641 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.664 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.656 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.634 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.297) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.645 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.655 (+/-0.292) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.658 (+/-0.292) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.634 (+/-0.321) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.668 (+/-0.295) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.645 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.658 (+/-0.292) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.635 (+/-0.308) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.658 (+/-0.292) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.643 (+/-0.310) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.666 (+/-0.297) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.641 (+/-0.307) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.632 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.643 (+/-0.306) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.656 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.647 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.624 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.666 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.643 (+/-0.306) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.631 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.624 (+/-0.319) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.667 (+/-0.301) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.641 (+/-0.307) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.630 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.668 (+/-0.300) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.698 (+/-0.352) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.643 (+/-0.310) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.706 (+/-0.353) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.652 (+/-0.313) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.639 (+/-0.309) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.698 (+/-0.352) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.683 (+/-0.314) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.627 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.638 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.681 (+/-0.372) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.613 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.662 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.224) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.240) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.317) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.297) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.327) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.323) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.663 (+/-0.317) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.637 (+/-0.324) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.238) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.236) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.637 (+/-0.324) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.689 (+/-0.298) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.639 (+/-0.238) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.236) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.689 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.237) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.240) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.662 (+/-0.315) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.315) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.665 (+/-0.225) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.239) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.235) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.239) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.689 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.239) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.662 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.239) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.639 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.10 0.17 0.12 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.688941792398384
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([20892961.30854038, 21281390.4598271 , 21677041.04819691,
22080047.33018901, 22490546.05835781, 22908676.52767773,
23334580.62281001, 23768402.86624874, 24210290.46736181,
24660393.37234341, 25118864.31509581]), 'kernel': ['rbf'], 'gamma': array([2.7542287e-10, 2.7542287e-09, 2.7542287e-08, 2.7542287e-07,
2.7542287e-06, 2.7542287e-05, 2.7542287e-04, 2.7542287e-03,
2.7542287e-02, 2.7542287e-01, 2.7542287e+00])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}, 0.688941792398384)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [7.05974460e+06 2.42342272e-06]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([20892961.30854038, 21281390.4598271 , 21677041.04819691,
22080047.33018901, 22490546.05835781, 22908676.52767773,
23334580.62281001, 23768402.86624874, 24210290.46736181,
24660393.37234341, 25118864.31509581]), 'kernel': ['rbf'], 'gamma': array([2.7542287e-10, 2.7542287e-09, 2.7542287e-08, 2.7542287e-07,
2.7542287e-06, 2.7542287e-05, 2.7542287e-04, 2.7542287e-03,
2.7542287e-02, 2.7542287e-01, 2.7542287e+00])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.666 (+/-0.297) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.666 (+/-0.297) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.674 (+/-0.311) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.672 (+/-0.439) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.658 (+/-0.329) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.677 (+/-0.432) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.677 (+/-0.432) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.677 (+/-0.432) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.510 (+/-0.251) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.314) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.510 (+/-0.252) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.314) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.509 (+/-0.253) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.314) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.509 (+/-0.253) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.314) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.533 (+/-0.208) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.314) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.533 (+/-0.208) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.631 (+/-0.319) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.533 (+/-0.208) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.532 (+/-0.208) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.532 (+/-0.208) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.531 (+/-0.207) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.531 (+/-0.208) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.10 0.17 0.12 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.688941792398384
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.300) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.599 (+/-0.302) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.623 (+/-0.379) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.666 (+/-0.297) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.599 (+/-0.302) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.666 (+/-0.297) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.624 (+/-0.379) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.674 (+/-0.311) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.598 (+/-0.302) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.623 (+/-0.379) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.600 (+/-0.302) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.598 (+/-0.302) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.658 (+/-0.329) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.600 (+/-0.302) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.598 (+/-0.302) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.598 (+/-0.302) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.470 (+/-0.282) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.465 (+/-0.288) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.468 (+/-0.283) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.463 (+/-0.290) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.653 (+/-0.303) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.464 (+/-0.286) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.487 (+/-0.291) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.463 (+/-0.289) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.653 (+/-0.303) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.461 (+/-0.290) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.483 (+/-0.294) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.653 (+/-0.303) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.459 (+/-0.292) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.458 (+/-0.293) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.10 0.17 0.12 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.688941792398384
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([22080047.33018901, 22161543.25959723, 22243339.98485111,
22325438.61616745, 22407840.26786055, 22490546.05835781,
22573557.11021449, 22656874.55012912, 22740499.50895897,
22824433.12173501, 22908676.52767773]), 'kernel': ['rbf'], 'gamma': array([2.75422870e-06, 4.36515832e-06, 6.91830971e-06, 1.09647820e-05,
1.73780083e-05, 2.75422870e-05, 4.36515832e-05, 6.91830971e-05,
1.09647820e-04, 1.73780083e-04, 2.75422870e-04])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}, 0.688941792398384)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [4.18130469e+05 5.08219768e-20]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 2.7542287033381745e-05, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 22490546.058357812, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9581856839121191
测试集中，预测为舞弊样本的有： (array([1252, 1255], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 2
训练模型SVC对测试样本的预测准确率： 0.9043231750531537
以上是第27次特征筛选。
第27次特征筛选，AUC值是： 0.5909090909090908
X_train_iter_svc.shape is: (1257, 25)
X_test_iter_svc.shape is: (1257, 25)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6413461538461539
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.672 (+/-0.392) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.798 (+/-0.437) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.714 (+/-0.418) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.632 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.643 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.659 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.618 (+/-0.322) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.723 (+/-0.358) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.661 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.714 (+/-0.351) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.385) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.710 (+/-0.363) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.290) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.372) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.646 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.643 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.371) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.625 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.671 (+/-0.378) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.294) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.682 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.640 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6823717948717949
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.722 (+/-0.473) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.748 (+/-0.449) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.748 (+/-0.449) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.773 (+/-0.423) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.731 (+/-0.422) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.423) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.473) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.748 (+/-0.449) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.723 (+/-0.423) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.723 (+/-0.423) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.698 (+/-0.383) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.437) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.731 (+/-0.422) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.673 (+/-0.330) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.714 (+/-0.418) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.375) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.417) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.689 (+/-0.382) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.698 (+/-0.383) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.375) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.374) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.383) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.373) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.708 (+/-0.423) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.774 (+/-0.458) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.694 (+/-0.354) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.706 (+/-0.383) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.380) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.671 (+/-0.379) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.671 (+/-0.380) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.764 (+/-0.478) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.714 (+/-0.418) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.691 (+/-0.364) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.694 (+/-0.354) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.375) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.660 (+/-0.318) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.673 (+/-0.381) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.670 (+/-0.381) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.643 (+/-0.320) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.806 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.420) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.684 (+/-0.353) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.669 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.376) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.383) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.669 (+/-0.381) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.753 (+/-0.491) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.421) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.354) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.676 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.373) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.329) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.630 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.668 (+/-0.382) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.632 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.295) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.237) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.235) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.235) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.319) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.641 (+/-0.235) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.295) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.698 (+/-0.304) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.320) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.319) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.641 (+/-0.235) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.295) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.322) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6979156360788193
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.798 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.382) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.710 (+/-0.363) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.798 (+/-0.437) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.372) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.714 (+/-0.351) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.358) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.398) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.701 (+/-0.351) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.374) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.677 (+/-0.374) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.437) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.727 (+/-0.397) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.701 (+/-0.351) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.714 (+/-0.351) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.382) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.669 (+/-0.373) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.848 (+/-0.460) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.773 (+/-0.416) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.739 (+/-0.392) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.317) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.651 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.376) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.383) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.723 (+/-0.417) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.716 (+/-0.401) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.290) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.639 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.377) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.388) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.735 (+/-0.455) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.674 (+/-0.376) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.656 (+/-0.369) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.330) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.638 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.627 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.387) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.764 (+/-0.478) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.721 (+/-0.463) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.673 (+/-0.391) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.646 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.648 (+/-0.284) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.630 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.382) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.389) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.764 (+/-0.478) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.696 (+/-0.439) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.677 (+/-0.432) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.631 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.624 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.623 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.389) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.627 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.418) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.748 (+/-0.449) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.663 (+/-0.390) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.653 (+/-0.381) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.646 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.647 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.630 (+/-0.326) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.619 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.389) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.631 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.372) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.748 (+/-0.449) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.393) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.653 (+/-0.380) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.630 (+/-0.279) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.646 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.621 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.618 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.623 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.628 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.671 (+/-0.378) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.667 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.295) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.683 (+/-0.296) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.314) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.294) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.296) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.682 (+/-0.293) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.682 (+/-0.294) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.293) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.682 (+/-0.295) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.317) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.294) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.292) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.665 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.288) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.665 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.236) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.308) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.307) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.657 (+/-0.319) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.235) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.694 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.235) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.655 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.677 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.655 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.08 0.17 0.11 6
1 0.99 0.98 0.99 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.695832302745486
循环迭代之前，delta is: [2.98107171e+06 2.11758237e-22]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([2511886.43150958, 2754228.70333817, 3019951.72040201,
3311311.21482591, 3630780.54770102, 3981071.70553498,
4365158.32240166, 4786300.92322638, 5248074.60249773,
5754399.37337156, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([6.30957344e-07, 6.91830971e-07, 7.58577575e-07, 8.31763771e-07,
9.12010839e-07, 1.00000000e-06, 1.09647820e-06, 1.20226443e-06,
1.31825674e-06, 1.44543977e-06, 1.58489319e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.774 (+/-0.458) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.702 (+/-0.421) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.756 (+/-0.432) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.681 (+/-0.373) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.789 (+/-0.443) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.684 (+/-0.372) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.652 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.291) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.689 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.774 (+/-0.458) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.685 (+/-0.384) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.698 (+/-0.367) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.765 (+/-0.422) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.710 (+/-0.363) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.684 (+/-0.372) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.652 (+/-0.313) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.647 (+/-0.308) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.681 (+/-0.373) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.773 (+/-0.461) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.699 (+/-0.423) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.404) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.765 (+/-0.422) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.684 (+/-0.372) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.652 (+/-0.313) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.681 (+/-0.373) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.756 (+/-0.438) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.386) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.664 (+/-0.299) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.764 (+/-0.421) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.684 (+/-0.372) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.648 (+/-0.306) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.689 (+/-0.375) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.731 (+/-0.408) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.374) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.639 (+/-0.309) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.756 (+/-0.425) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.684 (+/-0.372) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.645 (+/-0.306) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.689 (+/-0.375) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.691 (+/-0.364) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.685 (+/-0.384) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.658 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.668 (+/-0.371) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.706 (+/-0.418) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.374) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.648 (+/-0.306) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.656 (+/-0.312) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.689 (+/-0.375) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.675 (+/-0.351) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.669 (+/-0.373) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.660 (+/-0.290) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.756 (+/-0.425) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.374) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.651 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.698 (+/-0.376) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.673 (+/-0.351) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.372) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.660 (+/-0.290) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.764 (+/-0.421) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.702 (+/-0.355) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.321) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.651 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.698 (+/-0.376) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.673 (+/-0.351) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.672 (+/-0.371) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.664 (+/-0.291) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.668 (+/-0.371) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.764 (+/-0.421) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.702 (+/-0.355) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.321) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.651 (+/-0.308) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.643 (+/-0.301) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.698 (+/-0.376) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.676 (+/-0.351) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.371) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.658 (+/-0.290) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.668 (+/-0.371) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.764 (+/-0.421) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.321) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.681 (+/-0.373) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.651 (+/-0.308) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.698 (+/-0.376) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.684 (+/-0.353) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.372) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.635 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.764 (+/-0.421) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.669 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.681 (+/-0.373) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.645 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.698 (+/-0.376) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.682 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.682 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.682 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.681 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.315) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.682 (+/-0.295) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.699 (+/-0.309) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.699 (+/-0.307) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.315) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.316) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.682 (+/-0.295) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.699 (+/-0.309) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.682 (+/-0.294) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.316) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.294) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.305) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.313) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.664 (+/-0.313) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.682 (+/-0.296) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.304) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.312) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.316) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.304) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.292) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.313) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.293) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.682 (+/-0.296) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.304) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.315) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.317) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.291) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.312) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.293) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.304) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.315) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.317) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.291) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.313) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.294) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.304) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.315) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.317) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.291) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.312) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.292) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.682 (+/-0.296) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.305) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.665 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.315) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.315) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.317) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.656 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.682 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.665 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.664 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.12 0.17 0.14 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6993589743589744
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.646 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.641 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.655 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.658 (+/-0.329) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.701 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.648 (+/-0.284) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.326) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.622 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.647 (+/-0.289) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.630 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.646 (+/-0.317) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.691 (+/-0.355) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.656 (+/-0.293) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.327) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.701 (+/-0.351) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.656 (+/-0.291) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.651 (+/-0.308) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.326) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.622 (+/-0.298) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.630 (+/-0.302) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.620 (+/-0.304) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.637 (+/-0.301) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.689 (+/-0.357) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.648 (+/-0.287) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.327) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.701 (+/-0.351) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.648 (+/-0.284) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.648 (+/-0.306) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.658 (+/-0.329) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.620 (+/-0.299) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.630 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.618 (+/-0.305) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.629 (+/-0.292) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.355) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.645 (+/-0.287) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.652 (+/-0.322) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.701 (+/-0.351) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.656 (+/-0.291) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.648 (+/-0.306) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.658 (+/-0.329) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.624 (+/-0.300) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.626 (+/-0.302) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.627 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.631 (+/-0.291) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.353) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.629 (+/-0.300) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.652 (+/-0.322) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.350) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.660 (+/-0.290) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.648 (+/-0.306) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.656 (+/-0.330) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.623 (+/-0.301) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.624 (+/-0.302) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.625 (+/-0.315) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.631 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.633 (+/-0.298) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.647 (+/-0.285) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.643 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.637 (+/-0.299) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.658 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.648 (+/-0.306) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.656 (+/-0.330) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.623 (+/-0.301) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.624 (+/-0.302) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.624 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.288) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.298) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.650 (+/-0.284) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.652 (+/-0.322) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.353) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.658 (+/-0.292) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.645 (+/-0.306) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.655 (+/-0.331) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.623 (+/-0.301) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.623 (+/-0.302) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.632 (+/-0.324) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.288) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.298) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.645 (+/-0.284) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.652 (+/-0.322) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.689 (+/-0.357) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.660 (+/-0.299) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.649 (+/-0.313) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.655 (+/-0.331) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.623 (+/-0.301) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.623 (+/-0.302) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.631 (+/-0.325) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.634 (+/-0.280) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.629 (+/-0.298) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.645 (+/-0.284) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.635 (+/-0.299) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.689 (+/-0.357) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.302) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.655 (+/-0.331) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.623 (+/-0.301) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.620 (+/-0.303) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.631 (+/-0.325) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.638 (+/-0.281) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.629 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.644 (+/-0.285) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.635 (+/-0.299) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.689 (+/-0.357) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.649 (+/-0.296) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.645 (+/-0.318) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.621 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.620 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.631 (+/-0.325) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.646 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.629 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.618 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.643 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.689 (+/-0.357) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.639 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.620 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.618 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.630 (+/-0.326) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.671 (+/-0.378) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.681 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.662 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.679 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.661 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.301) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.293) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.698 (+/-0.309) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.318) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.662 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.662 (+/-0.318) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.660 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.287) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.293) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.698 (+/-0.309) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.293) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.663 (+/-0.318) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.661 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.661 (+/-0.318) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.660 (+/-0.313) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.288) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.293) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.316) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.681 (+/-0.296) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.663 (+/-0.318) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.661 (+/-0.315) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.661 (+/-0.317) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.660 (+/-0.313) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.288) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.663 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.316) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.681 (+/-0.296) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.663 (+/-0.319) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.661 (+/-0.315) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.661 (+/-0.317) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.659 (+/-0.311) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.663 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.663 (+/-0.319) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.660 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.660 (+/-0.318) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.659 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.290) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.313) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.294) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.316) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.680 (+/-0.295) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.662 (+/-0.319) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.660 (+/-0.317) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.659 (+/-0.319) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.658 (+/-0.311) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.290) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.313) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.294) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.317) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.680 (+/-0.295) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.662 (+/-0.319) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.660 (+/-0.317) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.659 (+/-0.319) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.657 (+/-0.311) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.290) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.294) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.316) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.680 (+/-0.296) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.695 (+/-0.307) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.662 (+/-0.320) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.659 (+/-0.318) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.659 (+/-0.317) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.657 (+/-0.311) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.290) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.679 (+/-0.294) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.679 (+/-0.296) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.695 (+/-0.307) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.662 (+/-0.319) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.659 (+/-0.318) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.658 (+/-0.318) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.657 (+/-0.311) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.654 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.679 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.661 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.658 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.658 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.656 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6975961538461539
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([2511886.43150958, 2558585.88690565, 2606153.5499989 ,
2654605.56197554, 2703958.36410884, 2754228.70333816,
2805433.63795172, 2857590.54337495, 2910717.11806661,
2964831.38952434, 3019951.72040201]), 'kernel': ['rbf'], 'gamma': array([8.31763771e-07, 8.47227414e-07, 8.62978548e-07, 8.79022517e-07,
8.95364766e-07, 9.12010839e-07, 9.28966387e-07, 9.46237161e-07,
9.63829024e-07, 9.81747943e-07, 1.00000000e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}, 0.6993589743589744)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [1.22684300e+06 8.79891606e-08]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([2511886.43150958, 2558585.88690565, 2606153.5499989 ,
2654605.56197554, 2703958.36410884, 2754228.70333816,
2805433.63795172, 2857590.54337495, 2910717.11806661,
2964831.38952434, 3019951.72040201]), 'kernel': ['rbf'], 'gamma': array([8.31763771e-07, 8.47227414e-07, 8.62978548e-07, 8.79022517e-07,
8.95364766e-07, 9.12010839e-07, 9.28966387e-07, 9.46237161e-07,
9.63829024e-07, 9.81747943e-07, 1.00000000e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.681 (+/-0.373) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.699 (+/-0.423) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.773 (+/-0.416) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.789 (+/-0.443) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.694 (+/-0.354) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.373) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.699 (+/-0.423) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.773 (+/-0.416) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.789 (+/-0.443) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.702 (+/-0.355) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.702 (+/-0.421) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.764 (+/-0.421) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.789 (+/-0.443) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.702 (+/-0.355) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.699 (+/-0.423) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.764 (+/-0.421) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.764 (+/-0.421) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.694 (+/-0.354) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.699 (+/-0.423) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.764 (+/-0.421) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.702 (+/-0.363) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.702 (+/-0.421) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.756 (+/-0.425) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.710 (+/-0.363) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.698 (+/-0.424) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.764 (+/-0.421) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.710 (+/-0.363) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.702 (+/-0.421) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.756 (+/-0.425) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.383) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.748 (+/-0.395) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.702 (+/-0.355) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.706 (+/-0.418) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.702 (+/-0.421) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.756 (+/-0.425) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.383) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.694 (+/-0.354) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.706 (+/-0.418) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.702 (+/-0.421) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.756 (+/-0.425) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.764 (+/-0.421) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.383) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.748 (+/-0.395) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.694 (+/-0.354) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.706 (+/-0.418) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.699 (+/-0.423) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.756 (+/-0.425) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.383) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.698 (+/-0.383) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.694 (+/-0.354) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.683 (+/-0.296) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.307) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.683 (+/-0.296) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.307) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.683 (+/-0.296) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.294) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.295) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.296) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.699 (+/-0.307) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.699 (+/-0.307) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.296) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.682 (+/-0.295) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.307) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.296) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.294) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.296) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.683 (+/-0.296) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.682 (+/-0.295) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.294) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.296) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.294) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.12 0.17 0.14 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6993589743589744
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.658 (+/-0.329) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.740 (+/-0.392) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.691 (+/-0.356) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.650 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.702 (+/-0.354) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.691 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.658 (+/-0.329) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.740 (+/-0.392) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.691 (+/-0.356) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.650 (+/-0.313) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.694 (+/-0.353) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.691 (+/-0.355) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.291) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.740 (+/-0.392) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.709 (+/-0.351) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.687 (+/-0.358) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.650 (+/-0.313) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.691 (+/-0.355) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.291) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.687 (+/-0.358) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.649 (+/-0.314) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.689 (+/-0.357) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.687 (+/-0.358) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.649 (+/-0.314) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.668 (+/-0.371) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.687 (+/-0.358) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.649 (+/-0.314) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.291) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.668 (+/-0.371) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.687 (+/-0.358) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.649 (+/-0.314) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.291) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.643 (+/-0.316) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.637 (+/-0.307) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.640 (+/-0.308) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.291) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.643 (+/-0.316) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.681 (+/-0.373) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.636 (+/-0.308) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.640 (+/-0.308) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.643 (+/-0.316) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.681 (+/-0.373) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.637 (+/-0.307) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.640 (+/-0.308) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.643 (+/-0.316) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.681 (+/-0.373) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.668 (+/-0.371) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.636 (+/-0.308) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.640 (+/-0.308) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.685 (+/-0.360) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.671 (+/-0.378) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.699 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.681 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.681 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.699 (+/-0.310) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.681 (+/-0.295) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.292) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.312) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.681 (+/-0.293) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.293) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.307) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.699 (+/-0.310) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.681 (+/-0.295) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.291) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.312) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.293) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.307) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.297) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.681 (+/-0.295) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.291) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.311) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.293) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.297) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.292) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.311) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.297) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.292) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.311) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.307) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.681 (+/-0.298) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.292) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.311) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.307) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.681 (+/-0.298) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.663 (+/-0.311) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.663 (+/-0.311) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.307) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.317) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.681 (+/-0.298) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.663 (+/-0.311) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.663 (+/-0.311) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.317) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.297) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.681 (+/-0.296) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.663 (+/-0.312) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.663 (+/-0.311) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.317) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.681 (+/-0.298) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.663 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.663 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.680 (+/-0.292) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6988782051282051
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([2654605.56197554, 2664403.5277249 , 2674237.65708899,
2684108.08354523, 2694014.94106366, 2703958.36410884,
2713938.48764159, 2723955.44712086, 2734009.37850561,
2744100.41825657, 2754228.70333816]), 'kernel': ['rbf'], 'gamma': array([8.95364766e-07, 8.98669495e-07, 9.01986422e-07, 9.05315592e-07,
9.08657049e-07, 9.12010839e-07, 9.15377008e-07, 9.18755602e-07,
9.22146665e-07, 9.25550245e-07, 9.28966387e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}, 0.6993589743589744)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [50270.33922933 0. ]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 9.120108393559093e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 2703958.364108842, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9347980155917789
测试集中，预测为舞弊样本的有： (array([1255], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 1
训练模型SVC对测试样本的预测准确率： 0.8936924167257264
以上是第28次特征筛选。
第28次特征筛选，AUC值是： 0.5454545454545454
X_train_iter_svc.shape is: (1257, 24)
X_test_iter_svc.shape is: (1257, 24)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6413461538461539
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.333) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.375) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.798 (+/-0.437) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.723 (+/-0.417) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.627 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.651 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.636 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.641 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.235) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.224) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.586 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.627 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.643 (+/-0.391) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.673 (+/-0.330) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.673 (+/-0.330) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.387) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.702 (+/-0.355) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.656 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.389) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.372) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.645 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.370) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.242) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.243) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.636 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.682 (+/-0.294) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.235) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.224) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.586 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6818910256410255
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.772 (+/-0.473) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.748 (+/-0.449) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.747 (+/-0.449) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.731 (+/-0.422) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.731 (+/-0.422) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.423) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.473) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.748 (+/-0.449) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.731 (+/-0.422) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.698 (+/-0.383) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.698 (+/-0.383) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.449) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.731 (+/-0.422) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.698 (+/-0.383) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.698 (+/-0.383) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.383) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.417) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.685 (+/-0.384) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.698 (+/-0.383) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.326) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.417) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.773 (+/-0.416) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.388) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.326) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.735 (+/-0.455) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.702 (+/-0.363) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.706 (+/-0.383) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.648 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.646 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.648 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.760 (+/-0.482) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.714 (+/-0.418) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.708 (+/-0.401) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.685 (+/-0.352) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.376) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.635 (+/-0.310) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.653 (+/-0.333) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.632 (+/-0.310) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.806 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.421) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.684 (+/-0.353) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.662 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.375) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.633 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.668 (+/-0.382) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.629 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.753 (+/-0.491) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.685 (+/-0.384) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.354) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.676 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.375) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.645 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.632 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.668 (+/-0.383) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.627 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.641 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.235) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.237) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.640 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.638 (+/-0.237) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.317) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.317) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.295) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.685 (+/-0.384) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.702 (+/-0.355) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.798 (+/-0.437) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.689 (+/-0.374) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.714 (+/-0.359) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.358) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.398) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.651 (+/-0.308) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.317) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.681 (+/-0.372) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.823 (+/-0.451) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.353) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.317) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.387) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.669 (+/-0.373) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.848 (+/-0.460) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.764 (+/-0.421) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.739 (+/-0.392) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.651 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.673 (+/-0.370) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.376) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.385) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.691 (+/-0.356) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.631 (+/-0.298) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.379) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.389) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.735 (+/-0.455) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.674 (+/-0.376) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.654 (+/-0.370) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.626 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.656 (+/-0.389) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.772 (+/-0.473) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.721 (+/-0.463) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.673 (+/-0.391) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.638 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.646 (+/-0.285) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.639 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.382) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.623 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.764 (+/-0.478) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.773 (+/-0.474) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.746 (+/-0.451) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.676 (+/-0.433) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.639 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.649 (+/-0.285) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.633 (+/-0.323) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.620 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.317) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.689 (+/-0.374) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.748 (+/-0.449) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.663 (+/-0.390) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.652 (+/-0.381) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.647 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.619 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.628 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.372) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.748 (+/-0.449) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.393) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.648 (+/-0.382) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.630 (+/-0.279) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.645 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.618 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.294) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.683 (+/-0.296) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.314) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.295) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.296) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.313) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.314) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.294) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.314) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.314) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.313) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.294) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.292) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.665 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.288) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.656 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.664 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.287) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.660 (+/-0.319) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.655 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.236) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.681 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.679 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.312) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.235) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.678 (+/-0.293) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.657 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.655 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.235) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.655 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.677 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.655 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.655 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
循环迭代之前，delta is: [3.69042656e+05 7.48811357e-07]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([ 398107.1705535 , 436515.83224017, 478630.09232264,
524807.46024977, 575439.93733716, 630957.34448019,
691830.97091894, 758577.57502918, 831763.77110267,
912010.83935591, 1000000. ]), 'kernel': ['rbf'], 'gamma': array([1.58489319e-07, 1.73780083e-07, 1.90546072e-07, 2.08929613e-07,
2.29086765e-07, 2.51188643e-07, 2.75422870e-07, 3.01995172e-07,
3.31131121e-07, 3.63078055e-07, 3.98107171e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6666666666666665
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.773 (+/-0.474) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.773 (+/-0.474) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.773 (+/-0.474) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.848 (+/-0.460) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.773 (+/-0.474) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.764 (+/-0.478) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.848 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.683 (+/-0.296) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([478630.09232264, 487528.49010339, 496592.32145034, 505824.66200311,
515228.64458176, 524807.46024977, 534564.35939697, 544502.65284242,
554625.71295791, 564936.9748123 , 575439.93733716]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-07, 2.33345806e-07, 2.37684029e-07, 2.42102905e-07,
2.46603934e-07, 2.51188643e-07, 2.55858589e-07, 2.60615355e-07,
2.65460556e-07, 2.70395836e-07, 2.75422870e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}, 0.6833333333333332)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [1.06149884e+05 6.35274710e-22]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([478630.09232264, 487528.49010339, 496592.32145034, 505824.66200311,
515228.64458176, 524807.46024977, 534564.35939697, 544502.65284242,
554625.71295791, 564936.9748123 , 575439.93733716]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-07, 2.33345806e-07, 2.37684029e-07, 2.42102905e-07,
2.46603934e-07, 2.51188643e-07, 2.55858589e-07, 2.60615355e-07,
2.65460556e-07, 2.70395836e-07, 2.75422870e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6666666666666665
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
发现最优参数C为原先的最大/最小值，直接重新设置超参。
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([4.78630092e+00, 4.78630092e+01, 4.78630092e+02, 4.78630092e+03,
4.78630092e+04, 4.78630092e+05, 4.78630092e+06, 4.78630092e+07,
4.78630092e+08, 4.78630092e+09, 4.78630092e+10]), 'kernel': ['rbf'], 'gamma': array([2.51188643e-07, 2.52115763e-07, 2.53046305e-07, 2.53980281e-07,
2.54917705e-07, 2.55858589e-07, 2.56802945e-07, 2.57750787e-07,
2.58702127e-07, 2.59656979e-07, 2.60615355e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}, 0.6833333333333332)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [4.61773679e+04 4.66994554e-09]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([4.78630092e+00, 4.78630092e+01, 4.78630092e+02, 4.78630092e+03,
4.78630092e+04, 4.78630092e+05, 4.78630092e+06, 4.78630092e+07,
4.78630092e+08, 4.78630092e+09, 4.78630092e+10]), 'kernel': ['rbf'], 'gamma': array([2.51188643e-07, 2.52115763e-07, 2.53046305e-07, 2.53980281e-07,
2.54917705e-07, 2.55858589e-07, 2.56802945e-07, 2.57750787e-07,
2.58702127e-07, 2.59656979e-07, 2.60615355e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.810 (+/-0.465) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.773 (+/-0.474) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.760 (+/-0.482) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.693 (+/-0.446) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.760 (+/-0.482) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.743 (+/-0.459) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.743 (+/-0.459) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.760 (+/-0.482) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.773 (+/-0.474) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.773 (+/-0.474) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.743 (+/-0.459) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.736 (+/-0.468) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.686 (+/-0.452) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.736 (+/-0.468) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.753 (+/-0.492) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.688 (+/-0.390) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.741 (+/-0.402) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.741 (+/-0.402) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.691 (+/-0.387) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.679 (+/-0.389) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.674 (+/-0.392) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.678 (+/-0.391) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.736 (+/-0.468) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.686 (+/-0.452) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.736 (+/-0.468) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.736 (+/-0.468) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.686 (+/-0.393) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.736 (+/-0.411) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.731 (+/-0.407) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.733 (+/-0.405) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.712 (+/-0.372) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.686 (+/-0.393) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.686 (+/-0.393) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.736 (+/-0.468) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.686 (+/-0.452) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.736 (+/-0.468) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.753 (+/-0.492) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.686 (+/-0.393) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.739 (+/-0.405) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.731 (+/-0.407) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.731 (+/-0.407) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.712 (+/-0.372) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.686 (+/-0.393) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.686 (+/-0.393) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.736 (+/-0.468) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.686 (+/-0.452) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.736 (+/-0.468) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.753 (+/-0.492) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.686 (+/-0.393) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.736 (+/-0.411) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.731 (+/-0.407) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.731 (+/-0.407) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.712 (+/-0.372) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.686 (+/-0.393) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.686 (+/-0.393) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.683 (+/-0.296) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.666 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.641 (+/-0.324) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.666 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.666 (+/-0.315) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.666 (+/-0.315) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.666 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.667 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.667 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.666 (+/-0.315) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.657 (+/-0.309) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.632 (+/-0.315) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.657 (+/-0.309) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.657 (+/-0.310) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.665 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.682 (+/-0.295) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.682 (+/-0.295) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.666 (+/-0.315) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.665 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.665 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.665 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.657 (+/-0.309) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.632 (+/-0.315) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.657 (+/-0.309) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.657 (+/-0.309) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.665 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.681 (+/-0.294) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.682 (+/-0.294) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.682 (+/-0.295) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.681 (+/-0.294) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.665 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.665 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.657 (+/-0.309) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.632 (+/-0.315) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.657 (+/-0.309) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.657 (+/-0.310) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.665 (+/-0.314) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.682 (+/-0.295) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.682 (+/-0.294) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.682 (+/-0.294) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.681 (+/-0.294) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.665 (+/-0.314) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.665 (+/-0.314) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.657 (+/-0.309) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.632 (+/-0.315) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.657 (+/-0.309) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.657 (+/-0.310) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.665 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.681 (+/-0.294) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.682 (+/-0.294) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.682 (+/-0.294) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.681 (+/-0.294) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.665 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.665 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6828525641025641
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.848 (+/-0.460) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.696 (+/-0.439) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.646 (+/-0.403) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.662 (+/-0.444) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.662 (+/-0.444) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.663 (+/-0.391) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.688 (+/-0.432) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.688 (+/-0.432) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.669 (+/-0.395) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.668 (+/-0.396) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.668 (+/-0.396) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.669 (+/-0.395) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.650 (+/-0.397) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.650 (+/-0.397) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.655 (+/-0.394) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.642 (+/-0.405) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.648 (+/-0.398) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.654 (+/-0.394) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.651 (+/-0.396) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.658 (+/-0.395) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.651 (+/-0.396) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.651 (+/-0.396) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.654 (+/-0.395) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.625 (+/-0.332) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.650 (+/-0.397) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.395) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.642 (+/-0.405) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.623 (+/-0.334) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.628 (+/-0.331) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.650 (+/-0.397) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.654 (+/-0.395) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.651 (+/-0.396) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.650 (+/-0.397) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.654 (+/-0.395) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.625 (+/-0.332) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.650 (+/-0.397) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.395) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.642 (+/-0.406) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.623 (+/-0.334) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.627 (+/-0.331) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.650 (+/-0.398) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.653 (+/-0.396) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.651 (+/-0.396) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.650 (+/-0.397) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.654 (+/-0.395) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.625 (+/-0.332) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.650 (+/-0.397) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.395) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.641 (+/-0.406) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.623 (+/-0.334) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.627 (+/-0.331) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.650 (+/-0.398) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.653 (+/-0.396) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.651 (+/-0.396) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.650 (+/-0.397) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.654 (+/-0.395) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.683 (+/-0.296) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.656 (+/-0.308) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.639 (+/-0.322) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.640 (+/-0.323) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.640 (+/-0.323) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.664 (+/-0.313) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.664 (+/-0.314) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.664 (+/-0.314) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.664 (+/-0.313) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.663 (+/-0.314) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.664 (+/-0.314) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.664 (+/-0.314) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.654 (+/-0.308) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.654 (+/-0.308) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.655 (+/-0.308) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.638 (+/-0.323) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.662 (+/-0.313) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.663 (+/-0.313) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.663 (+/-0.313) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.663 (+/-0.313) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.662 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.662 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.662 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.654 (+/-0.308) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.654 (+/-0.308) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.308) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.638 (+/-0.323) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.661 (+/-0.313) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.662 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.662 (+/-0.313) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.663 (+/-0.312) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.662 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.662 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.662 (+/-0.315) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.654 (+/-0.308) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.654 (+/-0.308) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.308) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.638 (+/-0.323) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.661 (+/-0.313) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.662 (+/-0.313) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.662 (+/-0.313) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.663 (+/-0.312) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.662 (+/-0.315) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.662 (+/-0.314) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.662 (+/-0.314) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.654 (+/-0.308) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.654 (+/-0.308) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.308) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.638 (+/-0.323) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.661 (+/-0.313) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.662 (+/-0.313) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.662 (+/-0.313) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.663 (+/-0.312) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.662 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.662 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.662 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([ 47863.00923226, 75857.75750292, 120226.44346174,
190546.07179632, 301995.1720402 , 478630.09232264,
758577.57502918, 1202264.43461741, 1905460.71796325,
3019951.72040201, 4786300.92322638]), 'kernel': ['rbf'], 'gamma': array([2.51188643e-07, 2.51373794e-07, 2.51559081e-07, 2.51744505e-07,
2.51930066e-07, 2.52115763e-07, 2.52301597e-07, 2.52487568e-07,
2.52673677e-07, 2.52859922e-07, 2.53046305e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}, 0.6833333333333332)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [4.65661287e-10 3.74282561e-09]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 2.5211576308074103e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 478630.092322638, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9362154500354358
测试集中，预测为舞弊样本的有： (array([ 370, 658, 1246, 1247, 1248, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 7
训练模型SVC对测试样本的预测准确率： 0.9347980155917789
以上是第29次特征筛选。
第29次特征筛选，AUC值是： 0.7264701590544287
X_train_iter_svc.shape is: (1257, 23)
X_test_iter_svc.shape is: (1257, 23)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6413461538461539
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.333) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.706 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.748 (+/-0.449) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.723 (+/-0.417) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.656 (+/-0.284) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.659 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.666 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.225) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.223) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.10 0.17 0.12 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6807682001813835
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.627 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.313) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.714 (+/-0.359) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.714 (+/-0.359) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.387) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.652 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.656 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.372) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.629 (+/-0.283) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.654 (+/-0.775) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.677 (+/-0.374) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.624 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.242) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.225) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.223) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.640 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.657 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.449) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.689 (+/-0.436) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.723 (+/-0.423) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.723 (+/-0.423) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.672 (+/-0.392) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.722 (+/-0.473) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.449) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.723 (+/-0.423) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.723 (+/-0.423) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.672 (+/-0.392) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.626 (+/-0.318) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.722 (+/-0.473) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.449) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.723 (+/-0.423) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.723 (+/-0.423) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.375) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.672 (+/-0.392) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.722 (+/-0.473) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.449) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.723 (+/-0.423) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.723 (+/-0.423) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.375) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.672 (+/-0.392) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.622 (+/-0.336) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.723 (+/-0.423) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.723 (+/-0.423) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.375) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.672 (+/-0.392) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.657 (+/-0.390) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.723 (+/-0.423) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.723 (+/-0.423) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.706 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.656 (+/-0.392) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.618 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.236) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.615 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.641 (+/-0.236) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.235) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.641 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.589 (+/-0.230) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.641 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.589 (+/-0.231) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.234) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.615 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.232) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.234) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.666025641025641
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.797 (+/-0.492) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.382) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.673 (+/-0.330) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.797 (+/-0.492) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.326) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.673 (+/-0.330) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.797 (+/-0.492) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.706 (+/-0.383) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.326) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.673 (+/-0.330) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.651 (+/-0.308) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.797 (+/-0.492) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.706 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.326) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.673 (+/-0.330) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.651 (+/-0.308) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.626 (+/-0.313) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.797 (+/-0.492) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.706 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.651 (+/-0.308) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.659 (+/-0.385) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.645 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.797 (+/-0.492) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.706 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.669 (+/-0.373) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.659 (+/-0.390) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.313) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.797 (+/-0.492) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.748 (+/-0.449) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.706 (+/-0.383) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.714 (+/-0.359) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.673 (+/-0.330) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.689 (+/-0.374) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.669 (+/-0.373) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.663 (+/-0.383) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.647 (+/-0.387) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.618 (+/-0.322) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.797 (+/-0.492) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.748 (+/-0.449) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.706 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.714 (+/-0.359) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.673 (+/-0.330) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.689 (+/-0.374) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.669 (+/-0.373) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.662 (+/-0.384) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.659 (+/-0.387) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.622 (+/-0.319) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.748 (+/-0.449) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.706 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.714 (+/-0.359) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.673 (+/-0.330) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.689 (+/-0.374) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.669 (+/-0.373) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.662 (+/-0.384) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.658 (+/-0.387) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.630 (+/-0.328) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.618 (+/-0.322) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.706 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.673 (+/-0.330) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.689 (+/-0.374) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.669 (+/-0.373) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.662 (+/-0.384) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.658 (+/-0.387) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.655 (+/-0.392) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.622 (+/-0.319) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.714 (+/-0.359) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.330) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.689 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.669 (+/-0.373) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.663 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.660 (+/-0.385) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.625 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.642 (+/-0.236) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.666 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.642 (+/-0.236) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.665 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.642 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.665 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.665 (+/-0.314) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.642 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.665 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.325) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.642 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.325) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.611 (+/-0.242) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.642 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.636 (+/-0.327) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.242) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.236) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.316) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.682 (+/-0.295) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.661 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.634 (+/-0.327) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.242) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.642 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.316) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.682 (+/-0.295) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.661 (+/-0.314) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.659 (+/-0.314) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.635 (+/-0.325) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.585 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.316) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.682 (+/-0.295) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.661 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.658 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.635 (+/-0.325) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.610 (+/-0.242) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.586 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.661 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.658 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.635 (+/-0.325) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.635 (+/-0.324) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.586 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.234) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.661 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.660 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.660 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.636 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.235) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
循环迭代之前，delta is: [3.69042656e+01 5.84893192e-03]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([ 39.81071706, 43.65158322, 47.86300923, 52.48074602,
57.54399373, 63.09573445, 69.18309709, 75.8577575 ,
83.17637711, 91.20108394, 100. ]), 'kernel': ['rbf'], 'gamma': array([0.01 , 0.01096478, 0.01202264, 0.01318257, 0.0144544 ,
0.01584893, 0.01737801, 0.01905461, 0.02089296, 0.02290868,
0.02511886])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.722 (+/-0.473) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.722 (+/-0.473) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.722 (+/-0.473) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.722 (+/-0.473) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.722 (+/-0.473) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.722 (+/-0.473) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.722 (+/-0.473) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.723 (+/-0.423) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.723 (+/-0.423) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.723 (+/-0.423) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.723 (+/-0.423) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.723 (+/-0.423) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.723 (+/-0.423) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.706 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.685 (+/-0.384) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.677 (+/-0.374) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.727 (+/-0.397) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.710 (+/-0.363) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.326) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.706 (+/-0.383) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.685 (+/-0.384) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.677 (+/-0.374) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.727 (+/-0.397) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.710 (+/-0.363) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.326) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.326) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.706 (+/-0.383) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.685 (+/-0.384) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.677 (+/-0.374) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.727 (+/-0.397) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.710 (+/-0.363) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.326) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.326) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.706 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.685 (+/-0.384) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.677 (+/-0.374) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.727 (+/-0.397) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.664 (+/-0.326) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.326) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.706 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.685 (+/-0.384) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.677 (+/-0.374) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.727 (+/-0.397) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.710 (+/-0.363) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.664 (+/-0.326) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.706 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.685 (+/-0.384) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.677 (+/-0.374) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.727 (+/-0.397) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.710 (+/-0.363) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.685 (+/-0.384) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.677 (+/-0.374) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.727 (+/-0.397) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.710 (+/-0.363) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.714 (+/-0.359) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.714 (+/-0.359) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.677 (+/-0.374) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.727 (+/-0.397) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.710 (+/-0.363) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.714 (+/-0.359) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.714 (+/-0.359) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.727 (+/-0.397) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.710 (+/-0.363) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.714 (+/-0.359) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.714 (+/-0.359) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.317) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.710 (+/-0.363) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.714 (+/-0.359) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.317) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.317) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.317) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.689 (+/-0.374) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.689 (+/-0.374) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.294) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.682 (+/-0.295) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.314) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.294) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.665 (+/-0.314) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.294) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.665 (+/-0.314) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.294) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.665 (+/-0.314) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.294) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.294) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.314) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.294) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.314) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.294) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.294) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([52.48074602, 53.45643594, 54.45026528, 55.4625713 , 56.49369748,
57.54399373, 58.61381645, 59.70352866, 60.81350013, 61.94410751,
63.09573445]), 'kernel': ['rbf'], 'gamma': array([0.01584893, 0.01614359, 0.01644372, 0.01674943, 0.01706082,
0.01737801, 0.01770109, 0.01803018, 0.01836538, 0.01870682,
0.01905461])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}, 0.6822115384615385)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [5.55174071e+00 1.52907636e-03]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([52.48074602, 53.45643594, 54.45026528, 55.4625713 , 56.49369748,
57.54399373, 58.61381645, 59.70352866, 60.81350013, 61.94410751,
63.09573445]), 'kernel': ['rbf'], 'gamma': array([0.01584893, 0.01614359, 0.01644372, 0.01674943, 0.01706082,
0.01737801, 0.01770109, 0.01803018, 0.01836538, 0.01870682,
0.01905461])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.748 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.748 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.748 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.748 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.748 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.748 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.316) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.316) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.316) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.316) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.316) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.316) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.727 (+/-0.397) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.727 (+/-0.397) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.727 (+/-0.397) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.735 (+/-0.402) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.735 (+/-0.402) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.714 (+/-0.359) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.664 (+/-0.326) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.727 (+/-0.397) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.727 (+/-0.397) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.735 (+/-0.402) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.735 (+/-0.402) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.710 (+/-0.363) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.710 (+/-0.363) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.710 (+/-0.363) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.710 (+/-0.363) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.714 (+/-0.359) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.714 (+/-0.359) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.727 (+/-0.397) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.735 (+/-0.402) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.735 (+/-0.402) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.710 (+/-0.363) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.710 (+/-0.363) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.710 (+/-0.363) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.710 (+/-0.363) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.714 (+/-0.359) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.714 (+/-0.359) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.735 (+/-0.402) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.735 (+/-0.402) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.710 (+/-0.363) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.710 (+/-0.363) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.710 (+/-0.363) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.710 (+/-0.363) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.714 (+/-0.359) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.664 (+/-0.326) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.735 (+/-0.402) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.710 (+/-0.363) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.710 (+/-0.363) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.710 (+/-0.363) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.664 (+/-0.326) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.664 (+/-0.326) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.710 (+/-0.363) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.710 (+/-0.363) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.710 (+/-0.363) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.664 (+/-0.326) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.710 (+/-0.363) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.710 (+/-0.363) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.664 (+/-0.326) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.664 (+/-0.326) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.710 (+/-0.363) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.664 (+/-0.326) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.664 (+/-0.326) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.673 (+/-0.330) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.664 (+/-0.326) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.664 (+/-0.326) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.673 (+/-0.330) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.673 (+/-0.330) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.664 (+/-0.326) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.664 (+/-0.326) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.673 (+/-0.330) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.673 (+/-0.330) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.673 (+/-0.330) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.664 (+/-0.326) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.682 (+/-0.294) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.294) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.294) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.294) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.294) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.294) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.315) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.666 (+/-0.315) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.315) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.666 (+/-0.315) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.315) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
发现最优参数C为原先的最大/最小值，直接重新设置超参。
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([5.2480746e-04, 5.2480746e-03, 5.2480746e-02, 5.2480746e-01,
5.2480746e+00, 5.2480746e+01, 5.2480746e+02, 5.2480746e+03,
5.2480746e+04, 5.2480746e+05, 5.2480746e+06]), 'kernel': ['rbf'], 'gamma': array([0.01644372, 0.01650441, 0.01656533, 0.01662647, 0.01668784,
0.01674943, 0.01681125, 0.0168733 , 0.01693558, 0.01699809,
0.01706082])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}, 0.6822115384615385)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [5.06324771e+00 6.28579527e-04]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([5.2480746e-04, 5.2480746e-03, 5.2480746e-02, 5.2480746e-01,
5.2480746e+00, 5.2480746e+01, 5.2480746e+02, 5.2480746e+03,
5.2480746e+04, 5.2480746e+05, 5.2480746e+06]), 'kernel': ['rbf'], 'gamma': array([0.01644372, 0.01650441, 0.01656533, 0.01662647, 0.01668784,
0.01674943, 0.01681125, 0.0168733 , 0.01693558, 0.01699809,
0.01706082])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.632 (+/-0.311) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6658653846153846
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.727 (+/-0.397) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.294) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.658 (+/-0.315) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.658 (+/-0.315) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.658 (+/-0.315) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([ 5.2480746 , 8.31763771, 13.18256739, 20.89296131,
33.11311215, 52.48074602, 83.17637711, 131.82567386,
208.92961309, 331.13112148, 524.80746025]), 'kernel': ['rbf'], 'gamma': array([0.01644372, 0.01645584, 0.01646797, 0.01648011, 0.01649225,
0.01650441, 0.01651658, 0.01652875, 0.01654093, 0.01655313,
0.01656533])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}, 0.6822115384615385)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [3.55271368e-14 2.45018904e-04]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 0.01650440985652279, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 52.48074602497723, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9468462083628633
测试集中，预测为舞弊样本的有： (array([ 8, 10, 11, 12, 13, 14, 19, 20, 21, 22, 24,
25, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
40, 41, 42, 43, 44, 47, 48, 49, 50, 51, 52,
53, 54, 58, 59, 60, 64, 65, 66, 67, 68, 71,
72, 73, 74, 75, 76, 77, 78, 79, 88, 90, 91,
92, 93, 98, 101, 107, 108, 109, 110, 111, 112, 113,
114, 115, 116, 117, 118, 119, 121, 127, 129, 135, 136,
142, 143, 144, 145, 146, 150, 151, 153, 156, 157, 158,
163, 164, 165, 166, 176, 177, 180, 181, 182, 183, 184,
185, 186, 187, 192, 193, 197, 199, 200, 201, 202, 204,
207, 208, 212, 213, 214, 215, 216, 219, 225, 231, 232,
233, 234, 235, 236, 237, 238, 239, 242, 243, 244, 245,
246, 247, 251, 252, 253, 254, 255, 256, 257, 258, 259,
260, 261, 262, 264, 268, 269, 270, 275, 276, 277, 278,
279, 280, 281, 283, 284, 285, 286, 287, 288, 299, 300,
301, 302, 306, 313, 316, 317, 319, 322, 323, 324, 325,
330, 331, 332, 333, 334, 338, 339, 340, 346, 347, 348,
349, 351, 352, 353, 354, 356, 358, 359, 360, 361, 362,
363, 364, 365, 366, 367, 368, 369, 370, 379, 383, 384,
385, 386, 388, 391, 392, 393, 394, 395, 396, 398, 399,
403, 404, 410, 414, 419, 420, 421, 422, 423, 426, 427,
429, 430, 431, 432, 433, 434, 438, 439, 440, 441, 442,
443, 444, 447, 449, 454, 455, 456, 457, 461, 463, 465,
466, 467, 468, 469, 471, 472, 473, 474, 475, 476, 477,
478, 479, 481, 482, 483, 484, 485, 486, 487, 488, 489,
490, 492, 496, 498, 499, 500, 501, 502, 503, 509, 513,
514, 515, 516, 518, 519, 520, 521, 529, 530, 532, 534,
535, 540, 541, 543, 544, 545, 546, 547, 549, 555, 557,
558, 562, 563, 564, 565, 566, 567, 568, 571, 572, 573,
582, 583, 584, 586, 587, 589, 590, 593, 595, 600, 601,
604, 605, 611, 612, 613, 614, 615, 616, 617, 618, 619,
624, 629, 630, 633, 634, 635, 641, 642, 643, 651, 653,
656, 657, 658, 661, 662, 664, 665, 666, 667, 668, 669,
674, 675, 676, 677, 680, 684, 685, 686, 687, 688, 691,
692, 693, 694, 695, 696, 697, 699, 701, 702, 706, 716,
717, 718, 720, 721, 722, 723, 724, 729, 735, 736, 742,
743, 752, 753, 754, 756, 758, 759, 760, 761, 769, 774,
775, 776, 777, 778, 786, 787, 788, 789, 790, 791, 792,
793, 794, 795, 796, 797, 807, 813, 814, 815, 816, 817,
819, 820, 825, 826, 827, 828, 829, 830, 840, 841, 843,
844, 845, 848, 851, 852, 853, 854, 855, 856, 857, 859,
862, 864, 872, 874, 875, 876, 877, 878, 879, 880, 881,
882, 883, 884, 885, 897, 898, 902, 903, 904, 905, 906,
907, 910, 911, 912, 913, 914, 916, 923, 928, 929, 930,
931, 932, 933, 934, 935, 948, 949, 950, 951, 952, 953,
954, 955, 956, 959, 960, 961, 963, 964, 965, 966, 967,
971, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985,
998, 1000, 1002, 1003, 1004, 1005, 1006, 1008, 1009, 1012, 1013,
1014, 1015, 1016, 1017, 1020, 1021, 1022, 1030, 1032, 1033, 1034,
1037, 1040, 1041, 1048, 1049, 1050, 1051, 1056, 1057, 1060, 1061,
1064, 1065, 1067, 1068, 1071, 1073, 1074, 1080, 1081, 1082, 1085,
1086, 1089, 1090, 1092, 1094, 1095, 1096, 1097, 1098, 1099, 1100,
1101, 1106, 1107, 1112, 1113, 1114, 1119, 1121, 1122, 1123, 1124,
1127, 1129, 1131, 1132, 1134, 1135, 1137, 1140, 1141, 1142, 1144,
1148, 1152, 1154, 1156, 1158, 1160, 1163, 1164, 1167, 1168, 1171,
1175, 1180, 1181, 1183, 1188, 1191, 1196, 1205, 1211, 1215, 1217,
1218, 1219, 1222, 1227, 1230, 1231, 1232, 1233, 1234, 1235, 1236,
1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1246, 1247, 1248,
1249, 1250, 1251, 1252, 1254, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 645
训练模型SVC对测试样本的预测准确率： 0.5393338058114813
以上是第30次特征筛选。
第30次特征筛选，AUC值是： 0.6997300452356633
X_train_iter_svc.shape is: (1257, 22)
X_test_iter_svc.shape is: (1257, 22)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6413461538461539
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.333) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.654 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.311) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.629 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.748 (+/-0.449) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.706 (+/-0.383) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.772 (+/-0.473) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.668 (+/-0.290) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.637 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.764 (+/-0.478) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.659 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.666 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.636 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.11 0.17 0.13 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6812489694121527
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.627 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.313) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.654 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.714 (+/-0.359) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.714 (+/-0.359) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.311) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.677 (+/-0.374) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.387) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.629 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.290) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.710 (+/-0.420) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.637 (+/-0.291) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.650 (+/-0.777) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.662 (+/-0.373) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.625 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.674 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.242) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.244) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.651 (+/-0.258) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.640 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.636 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.449) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.714 (+/-0.418) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.723 (+/-0.423) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.723 (+/-0.423) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.672 (+/-0.392) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.722 (+/-0.473) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.449) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.723 (+/-0.423) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.723 (+/-0.423) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.672 (+/-0.392) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.722 (+/-0.473) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.449) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.723 (+/-0.423) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.723 (+/-0.423) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.375) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.672 (+/-0.392) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.722 (+/-0.473) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.449) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.723 (+/-0.423) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.723 (+/-0.423) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.375) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.672 (+/-0.392) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.622 (+/-0.336) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.723 (+/-0.423) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.723 (+/-0.423) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.375) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.672 (+/-0.392) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.657 (+/-0.390) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.621 (+/-0.320) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.723 (+/-0.423) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.723 (+/-0.423) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.706 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.657 (+/-0.390) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.619 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.236) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.641 (+/-0.235) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.615 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.641 (+/-0.236) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.235) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.615 (+/-0.237) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.641 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.590 (+/-0.229) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.588 (+/-0.233) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.641 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.590 (+/-0.230) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.234) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.615 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.613 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.234) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.612 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.666025641025641
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.797 (+/-0.492) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.382) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.673 (+/-0.330) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.797 (+/-0.492) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.326) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.673 (+/-0.330) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.797 (+/-0.492) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.706 (+/-0.383) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.326) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.673 (+/-0.330) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.651 (+/-0.308) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.797 (+/-0.492) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.706 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.326) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.673 (+/-0.330) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.651 (+/-0.308) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.626 (+/-0.313) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.797 (+/-0.492) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.706 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.659 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.659 (+/-0.385) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.650 (+/-0.385) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.797 (+/-0.492) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.706 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.669 (+/-0.373) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.663 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.313) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.797 (+/-0.492) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.748 (+/-0.449) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.706 (+/-0.383) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.714 (+/-0.359) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.673 (+/-0.330) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.689 (+/-0.374) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.669 (+/-0.373) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.663 (+/-0.383) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.647 (+/-0.387) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.618 (+/-0.322) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.797 (+/-0.492) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.748 (+/-0.449) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.706 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.714 (+/-0.359) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.673 (+/-0.330) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.689 (+/-0.374) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.669 (+/-0.373) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.663 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.658 (+/-0.387) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.622 (+/-0.319) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.748 (+/-0.449) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.706 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.714 (+/-0.359) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.673 (+/-0.330) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.689 (+/-0.374) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.669 (+/-0.373) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.663 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.658 (+/-0.388) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.630 (+/-0.328) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.618 (+/-0.322) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.706 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.673 (+/-0.330) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.689 (+/-0.374) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.669 (+/-0.373) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.663 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.658 (+/-0.388) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.660 (+/-0.385) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.622 (+/-0.319) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.714 (+/-0.359) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.330) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.689 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.669 (+/-0.373) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.663 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.660 (+/-0.385) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.626 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.625 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.674 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.642 (+/-0.236) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.666 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.642 (+/-0.236) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.665 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.642 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.665 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.665 (+/-0.314) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.642 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.665 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.325) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.642 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.325) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.636 (+/-0.327) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.642 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.661 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.608 (+/-0.244) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.236) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.316) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.682 (+/-0.295) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.661 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.633 (+/-0.328) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.243) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.642 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.316) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.682 (+/-0.295) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.661 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.658 (+/-0.314) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.634 (+/-0.326) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.585 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.316) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.682 (+/-0.295) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.661 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.658 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.635 (+/-0.325) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.610 (+/-0.242) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.586 (+/-0.235) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.661 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.658 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.660 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.635 (+/-0.324) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.586 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.586 (+/-0.235) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.661 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.660 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.636 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.586 (+/-0.235) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
循环迭代之前，delta is: [3.69042656e+01 5.84893192e-03]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([ 39.81071706, 43.65158322, 47.86300923, 52.48074602,
57.54399373, 63.09573445, 69.18309709, 75.8577575 ,
83.17637711, 91.20108394, 100. ]), 'kernel': ['rbf'], 'gamma': array([0.01 , 0.01096478, 0.01202264, 0.01318257, 0.0144544 ,
0.01584893, 0.01737801, 0.01905461, 0.02089296, 0.02290868,
0.02511886])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.722 (+/-0.473) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.722 (+/-0.473) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.772 (+/-0.473) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.772 (+/-0.473) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.772 (+/-0.473) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.772 (+/-0.473) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.772 (+/-0.473) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.723 (+/-0.423) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.723 (+/-0.423) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.723 (+/-0.423) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.723 (+/-0.423) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.723 (+/-0.423) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.723 (+/-0.423) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.642 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.642 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.642 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.642 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.642 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.706 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.685 (+/-0.384) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.677 (+/-0.374) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.727 (+/-0.397) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.735 (+/-0.402) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.326) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.706 (+/-0.383) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.685 (+/-0.384) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.677 (+/-0.374) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.727 (+/-0.397) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.735 (+/-0.402) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.326) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.326) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.706 (+/-0.383) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.685 (+/-0.384) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.677 (+/-0.374) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.727 (+/-0.397) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.735 (+/-0.402) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.326) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.326) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.706 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.685 (+/-0.384) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.677 (+/-0.374) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.727 (+/-0.397) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.326) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.706 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.685 (+/-0.384) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.677 (+/-0.374) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.727 (+/-0.397) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.710 (+/-0.363) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.664 (+/-0.326) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.706 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.685 (+/-0.384) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.677 (+/-0.374) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.727 (+/-0.397) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.710 (+/-0.363) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.685 (+/-0.384) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.677 (+/-0.374) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.727 (+/-0.397) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.710 (+/-0.363) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.714 (+/-0.359) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.714 (+/-0.359) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.677 (+/-0.374) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.727 (+/-0.397) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.710 (+/-0.363) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.714 (+/-0.359) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.714 (+/-0.359) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.727 (+/-0.397) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.710 (+/-0.363) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.714 (+/-0.359) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.714 (+/-0.359) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.317) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.710 (+/-0.363) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.714 (+/-0.359) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.317) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.317) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.317) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.317) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.689 (+/-0.374) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.674 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.294) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.682 (+/-0.295) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.314) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.294) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.665 (+/-0.314) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.294) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.665 (+/-0.314) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.294) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.665 (+/-0.314) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.294) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.294) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.314) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.294) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.314) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.294) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.294) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
发现最优参数C为原先的最大/最小值，直接重新设置超参。
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([3.98107171e-04, 3.98107171e-03, 3.98107171e-02, 3.98107171e-01,
3.98107171e+00, 3.98107171e+01, 3.98107171e+02, 3.98107171e+03,
3.98107171e+04, 3.98107171e+05, 3.98107171e+06]), 'kernel': ['rbf'], 'gamma': array([0.02089296, 0.02128139, 0.02167704, 0.02208005, 0.02249055,
0.02290868, 0.02333458, 0.0237684 , 0.02421029, 0.02466039,
0.02511886])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}, 0.6822115384615385)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [2.32850174e+01 7.05974460e-03]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([3.98107171e-04, 3.98107171e-03, 3.98107171e-02, 3.98107171e-01,
3.98107171e+00, 3.98107171e+01, 3.98107171e+02, 3.98107171e+03,
3.98107171e+04, 3.98107171e+05, 3.98107171e+06]), 'kernel': ['rbf'], 'gamma': array([0.02089296, 0.02128139, 0.02167704, 0.02208005, 0.02249055,
0.02290868, 0.02333458, 0.0237684 , 0.02421029, 0.02466039,
0.02511886])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.632 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.587 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.234) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.586 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.586 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6658653846153846
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.727 (+/-0.397) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.735 (+/-0.402) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.735 (+/-0.402) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.735 (+/-0.402) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.735 (+/-0.402) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.735 (+/-0.402) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.710 (+/-0.363) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.710 (+/-0.363) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.660 (+/-0.327) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.660 (+/-0.327) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.664 (+/-0.326) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.627 (+/-0.313) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.674 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.294) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.665 (+/-0.314) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.665 (+/-0.314) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.666 (+/-0.315) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.658 (+/-0.315) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.658 (+/-0.315) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.658 (+/-0.315) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.636 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.587 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.234) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.586 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.586 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([ 3.98107171, 6.30957344, 10. , 15.84893192,
25.11886432, 39.81071706, 63.09573445, 100. ,
158.48931925, 251.18864315, 398.10717055]), 'kernel': ['rbf'], 'gamma': array([0.02089296, 0.02097008, 0.02104747, 0.02112516, 0.02120313,
0.02128139, 0.02135994, 0.02143878, 0.02151791, 0.02159733,
0.02167704])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}, 0.6822115384615385)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [1.42108547e-14 1.62728607e-03]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([ 3.98107171, 6.30957344, 10. , 15.84893192,
25.11886432, 39.81071706, 63.09573445, 100. ,
158.48931925, 251.18864315, 398.10717055]), 'kernel': ['rbf'], 'gamma': array([0.02089296, 0.02097008, 0.02104747, 0.02112516, 0.02120313,
0.02128139, 0.02135994, 0.02143878, 0.02151791, 0.02159733,
0.02167704])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.727 (+/-0.397) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.727 (+/-0.397) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.727 (+/-0.397) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.727 (+/-0.397) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.669 (+/-0.379) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.669 (+/-0.379) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.669 (+/-0.379) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.669 (+/-0.379) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.669 (+/-0.379) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.380) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.380) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.381) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.381) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.381) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.381) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.674 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.682 (+/-0.294) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.682 (+/-0.294) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.682 (+/-0.294) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.682 (+/-0.294) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.663 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.663 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.663 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.663 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.663 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.662 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.662 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.662 (+/-0.314) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.662 (+/-0.314) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.662 (+/-0.314) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.662 (+/-0.314) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([25.11886432, 27.54228703, 30.1995172 , 33.11311215, 36.30780548,
39.81071706, 43.65158322, 47.86300923, 52.48074602, 57.54399373,
63.09573445]), 'kernel': ['rbf'], 'gamma': array([0.02112516, 0.02114073, 0.02115631, 0.02117191, 0.02118751,
0.02120313, 0.02121876, 0.0212344 , 0.02125005, 0.02126572,
0.02128139])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}, 0.6822115384615385)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [7.10542736e-15 7.82592924e-05]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 0.021203131167398505, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 39.810717055349684, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9468462083628633
测试集中，预测为舞弊样本的有： (array([ 8, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21,
22, 23, 24, 25, 30, 31, 32, 33, 34, 35, 36,
37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48,
49, 50, 51, 52, 53, 54, 57, 58, 59, 60, 63,
64, 65, 66, 67, 68, 71, 72, 73, 74, 75, 76,
77, 78, 79, 84, 87, 88, 90, 91, 92, 93, 98,
101, 102, 105, 106, 107, 108, 109, 110, 111, 112, 113,
114, 115, 116, 117, 118, 119, 120, 121, 127, 128, 129,
135, 136, 141, 142, 143, 144, 145, 146, 150, 151, 153,
154, 155, 156, 157, 158, 162, 163, 164, 165, 166, 169,
176, 177, 180, 181, 182, 183, 184, 185, 186, 187, 192,
193, 195, 196, 197, 199, 200, 201, 202, 204, 205, 207,
208, 209, 212, 213, 214, 215, 216, 217, 218, 219, 225,
229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239,
242, 243, 244, 245, 246, 247, 251, 252, 253, 254, 255,
256, 257, 258, 259, 260, 261, 262, 264, 267, 268, 269,
270, 274, 275, 276, 277, 278, 279, 280, 281, 283, 284,
285, 286, 287, 288, 290, 295, 296, 298, 299, 300, 301,
302, 306, 307, 308, 313, 316, 317, 318, 319, 320, 321,
322, 323, 324, 325, 330, 331, 332, 333, 334, 338, 339,
340, 344, 346, 347, 348, 349, 351, 352, 353, 354, 356,
358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368,
369, 370, 374, 375, 379, 383, 384, 385, 386, 388, 391,
392, 393, 394, 395, 396, 398, 399, 403, 404, 410, 413,
414, 419, 420, 421, 422, 423, 426, 427, 428, 429, 430,
431, 432, 433, 434, 438, 439, 440, 441, 442, 443, 444,
446, 447, 448, 449, 450, 454, 455, 456, 457, 460, 461,
463, 465, 466, 467, 468, 469, 471, 472, 473, 474, 475,
476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486,
487, 488, 489, 490, 491, 492, 493, 496, 498, 499, 500,
501, 502, 503, 508, 509, 511, 512, 513, 514, 515, 516,
517, 518, 519, 520, 521, 529, 530, 532, 534, 535, 540,
541, 542, 543, 544, 545, 546, 547, 549, 551, 552, 555,
557, 558, 560, 561, 562, 563, 564, 565, 566, 567, 568,
570, 571, 572, 573, 578, 582, 583, 584, 586, 587, 589,
590, 592, 593, 594, 595, 599, 600, 601, 604, 605, 611,
612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622,
623, 624, 629, 630, 631, 633, 634, 635, 641, 642, 643,
646, 651, 653, 655, 656, 657, 658, 659, 660, 661, 662,
663, 664, 665, 666, 667, 668, 669, 673, 674, 675, 676,
677, 680, 681, 682, 683, 684, 685, 686, 687, 688, 691,
692, 693, 694, 695, 696, 697, 698, 699, 701, 702, 703,
705, 706, 713, 714, 715, 716, 717, 718, 720, 721, 722,
723, 724, 729, 732, 733, 734, 735, 736, 742, 743, 751,
752, 753, 754, 756, 758, 759, 760, 761, 769, 774, 775,
776, 777, 778, 785, 786, 787, 788, 789, 790, 791, 792,
793, 794, 795, 796, 797, 806, 807, 812, 813, 814, 815,
816, 817, 819, 820, 824, 825, 826, 827, 828, 829, 830,
840, 841, 843, 844, 845, 846, 847, 848, 849, 850, 851,
852, 853, 854, 855, 856, 857, 859, 862, 864, 872, 874,
875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885,
893, 897, 898, 901, 902, 903, 904, 905, 906, 907, 910,
911, 912, 913, 914, 916, 919, 922, 923, 924, 925, 927,
928, 929, 930, 931, 932, 933, 934, 935, 946, 947, 948,
949, 950, 951, 952, 953, 954, 955, 956, 959, 960, 961,
963, 964, 965, 966, 967, 969, 970, 971, 976, 977, 978,
979, 980, 981, 982, 983, 984, 985, 992, 993, 995, 998,
1000, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1011, 1012,
1013, 1014, 1015, 1016, 1017, 1020, 1021, 1022, 1030, 1032, 1033,
1034, 1035, 1036, 1037, 1039, 1040, 1041, 1047, 1048, 1049, 1050,
1051, 1055, 1056, 1057, 1060, 1061, 1064, 1065, 1067, 1068, 1071,
1073, 1074, 1080, 1081, 1082, 1085, 1086, 1089, 1090, 1091, 1092,
1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1105, 1106,
1107, 1112, 1113, 1114, 1117, 1119, 1121, 1122, 1123, 1124, 1125,
1127, 1129, 1131, 1132, 1134, 1135, 1137, 1138, 1140, 1141, 1142,
1144, 1148, 1152, 1154, 1156, 1157, 1158, 1160, 1163, 1164, 1166,
1167, 1168, 1170, 1171, 1175, 1180, 1181, 1183, 1188, 1191, 1192,
1196, 1200, 1205, 1208, 1211, 1214, 1215, 1216, 1217, 1218, 1219,
1222, 1226, 1227, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237,
1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248,
1249, 1250, 1251, 1252, 1254, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 777
训练模型SVC对测试样本的预测准确率： 0.4457831325301205
以上是第31次特征筛选。
第31次特征筛选，AUC值是： 0.6467605428279586
X_train_iter_svc.shape is: (1257, 21)
X_test_iter_svc.shape is: (1257, 21)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.748 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.666 (+/-0.316) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6663461538461538
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.748 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.748 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.317) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.627 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.673 (+/-0.330) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.449) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.374) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.352) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.374) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.635 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.316) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.316) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.636 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.641 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.542 (+/-0.171) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.682 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.614 (+/-0.244) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.542 (+/-0.171) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.681729738642922
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.384) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.619 (+/-0.322) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.323) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.298) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.624 (+/-0.316) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.298) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.624 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.627 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.373) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.654 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.389) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.599 (+/-0.399) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.292) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.599 (+/-0.745) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.400) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.611 (+/-0.305) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.244) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.657 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.294) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.657 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.636 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.562 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.274) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.173) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.662 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6812494847060763
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.368) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.373) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.373) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.374) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.372) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.651 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.373) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.677 (+/-0.374) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.648 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.437) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.631 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.668 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.373) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.329) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.437) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.706 (+/-0.360) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.764 (+/-0.422) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.677 (+/-0.374) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.670 (+/-0.381) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.652 (+/-0.335) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.645 (+/-0.318) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.401) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.437) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.698 (+/-0.409) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.731 (+/-0.394) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.660 (+/-0.374) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.668 (+/-0.382) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.642 (+/-0.330) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.629 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.647 (+/-0.459) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.437) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.427) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.698 (+/-0.352) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.381) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.668 (+/-0.383) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.381) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.629 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.647 (+/-0.459) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.492) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.384) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.427) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.373) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.377) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.651 (+/-0.336) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.381) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.647 (+/-0.459) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.672 (+/-0.452) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.384) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.427) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.374) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.377) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.337) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.381) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.401) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.672 (+/-0.452) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.384) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.372) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.373) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.621 (+/-0.302) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.637 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.381) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.401) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.672 (+/-0.452) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.384) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.372) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.374) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.621 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.622 (+/-0.302) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.637 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.381) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.647 (+/-0.459) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.672 (+/-0.452) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.384) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.372) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.374) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.621 (+/-0.317) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.621 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.633 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.381) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.635 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.641 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.237) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.295) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.318) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.320) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.542 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.656 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.327) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.237) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.656 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.320) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.326) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.656 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.320) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.542 (+/-0.171) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.326) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.673 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.542 (+/-0.171) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.326) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.238) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.672 (+/-0.328) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.637 (+/-0.324) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.326) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.238) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.672 (+/-0.328) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.637 (+/-0.324) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.848 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.735 (+/-0.402) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.654 (+/-0.293) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.628 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.616 (+/-0.288) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.621 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.629 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.656 (+/-0.389) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.374) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.372) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.637 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.611 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.623 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.618 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.647 (+/-0.459) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.789 (+/-0.443) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.668 (+/-0.371) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.648 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.605 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.635 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.625 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.622 (+/-0.404) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.781 (+/-0.474) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.758 (+/-0.429) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.641 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.639 (+/-0.297) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.688 (+/-0.357) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.624 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.657 (+/-0.388) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.623 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.397) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.773 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.705 (+/-0.426) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.619 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.639 (+/-0.287) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.651 (+/-0.294) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.647 (+/-0.385) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.599 (+/-0.399) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.768 (+/-0.475) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.704 (+/-0.427) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.618 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.614 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.636 (+/-0.292) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.390) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.400) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.764 (+/-0.477) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.686 (+/-0.426) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.300) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.623 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.619 (+/-0.304) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.613 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.646 (+/-0.387) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.400) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.764 (+/-0.477) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.650 (+/-0.380) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.623 (+/-0.299) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.612 (+/-0.304) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.613 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.646 (+/-0.387) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.400) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.764 (+/-0.477) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.647 (+/-0.382) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.613 (+/-0.298) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.625 (+/-0.297) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.305) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.613 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.613 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.400) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.764 (+/-0.477) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.690 (+/-0.431) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.610 (+/-0.298) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.620 (+/-0.298) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.612 (+/-0.304) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.592 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.613 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.400) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.420) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.610 (+/-0.298) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.290) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.611 (+/-0.305) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.592 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.613 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.295) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.294) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.665 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.659 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.682 (+/-0.295) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.664 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.295) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.679 (+/-0.295) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.655 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.565 (+/-0.202) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.678 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.657 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.563 (+/-0.198) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.654 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.654 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.320) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.653 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.657 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.559 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.653 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.651 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.657 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.557 (+/-0.182) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.655 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.653 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.651 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.657 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.555 (+/-0.177) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.655 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.669 (+/-0.332) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.554 (+/-0.175) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.664 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.669 (+/-0.331) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.323) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.553 (+/-0.173) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.680 (+/-0.295) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.669 (+/-0.331) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.323) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.662 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
发现最优参数C为原先的最大/最小值，直接重新设置超参。
循环迭代之前，delta is: [9.00000000e+06 7.48811357e-07]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06, 1.e+07, 1.e+08,
1.e+09, 1.e+10, 1.e+11]), 'kernel': ['rbf'], 'gamma': array([1.58489319e-07, 1.73780083e-07, 1.90546072e-07, 2.08929613e-07,
2.29086765e-07, 2.51188643e-07, 2.75422870e-07, 3.01995172e-07,
3.31131121e-07, 3.63078055e-07, 3.98107171e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.647 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.697 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.697 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.672 (+/-0.399) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.681 (+/-0.427) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.744 (+/-0.433) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.748 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.697 (+/-0.437) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.697 (+/-0.438) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.647 (+/-0.460) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.722 (+/-0.473) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.672 (+/-0.452) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.671 (+/-0.433) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.739 (+/-0.437) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.714 (+/-0.418) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.656 (+/-0.384) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.697 (+/-0.438) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.647 (+/-0.460) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.722 (+/-0.473) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.672 (+/-0.452) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.401) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.671 (+/-0.433) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.739 (+/-0.437) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.714 (+/-0.418) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.631 (+/-0.319) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.697 (+/-0.438) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.647 (+/-0.460) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.697 (+/-0.492) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.672 (+/-0.452) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.401) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.671 (+/-0.433) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.739 (+/-0.437) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.714 (+/-0.418) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.631 (+/-0.319) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.697 (+/-0.438) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.647 (+/-0.460) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.722 (+/-0.473) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.672 (+/-0.452) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.401) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.671 (+/-0.433) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.739 (+/-0.437) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.714 (+/-0.418) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.631 (+/-0.319) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.635 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.325) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.592 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.617 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.641 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.665 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.682 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.666 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.616 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.641 (+/-0.325) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.592 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.633 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.640 (+/-0.323) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.664 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.682 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.666 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.615 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.641 (+/-0.325) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.592 (+/-0.320) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.633 (+/-0.318) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.326) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.640 (+/-0.323) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.664 (+/-0.314) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.682 (+/-0.296) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.666 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.615 (+/-0.238) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.641 (+/-0.325) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.592 (+/-0.320) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.617 (+/-0.327) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.326) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.640 (+/-0.323) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.664 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.682 (+/-0.296) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.666 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.615 (+/-0.238) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.641 (+/-0.325) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.592 (+/-0.320) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.633 (+/-0.318) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.326) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.640 (+/-0.323) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.664 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.682 (+/-0.296) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.666 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.615 (+/-0.238) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822099925797676
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.768 (+/-0.475) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.748 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.723 (+/-0.461) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.664 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.689 (+/-0.436) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.704 (+/-0.427) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.669 (+/-0.365) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.652 (+/-0.374) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.659 (+/-0.368) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.608 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.618 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.723 (+/-0.417) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.723 (+/-0.461) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.666 (+/-0.441) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.664 (+/-0.389) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.697 (+/-0.420) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.658 (+/-0.373) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.644 (+/-0.381) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.625 (+/-0.305) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.587 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.610 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.723 (+/-0.417) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.721 (+/-0.463) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.666 (+/-0.441) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.660 (+/-0.389) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.690 (+/-0.431) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.374) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.643 (+/-0.382) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.629 (+/-0.304) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.586 (+/-0.313) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.611 (+/-0.299) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.764 (+/-0.477) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.723 (+/-0.417) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.721 (+/-0.463) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.657 (+/-0.451) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.660 (+/-0.389) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.665 (+/-0.383) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.374) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.643 (+/-0.382) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.629 (+/-0.304) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.586 (+/-0.313) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.611 (+/-0.299) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.723 (+/-0.417) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.721 (+/-0.463) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.657 (+/-0.451) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.660 (+/-0.389) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.672 (+/-0.372) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.656 (+/-0.372) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.643 (+/-0.382) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.629 (+/-0.304) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.586 (+/-0.313) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.611 (+/-0.299) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.666 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.616 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.632 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.657 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.680 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.679 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.680 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.638 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.663 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.666 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.631 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.648 (+/-0.340) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.680 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.679 (+/-0.294) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.677 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.678 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.611 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.662 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.665 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.631 (+/-0.318) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.648 (+/-0.340) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.663 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.679 (+/-0.293) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.677 (+/-0.292) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.695 (+/-0.304) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.611 (+/-0.311) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.662 (+/-0.312) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.665 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.614 (+/-0.328) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.648 (+/-0.340) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.663 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.679 (+/-0.293) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.677 (+/-0.292) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.695 (+/-0.304) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.611 (+/-0.310) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.662 (+/-0.312) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.665 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.614 (+/-0.328) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.648 (+/-0.340) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.680 (+/-0.294) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.679 (+/-0.293) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.677 (+/-0.292) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.695 (+/-0.304) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.611 (+/-0.310) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.662 (+/-0.312) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.662 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6947079314040729
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.00000000e+08, 1.58489319e+08, 2.51188643e+08, 3.98107171e+08,
6.30957344e+08, 1.00000000e+09, 1.58489319e+09, 2.51188643e+09,
3.98107171e+09, 6.30957344e+09, 1.00000000e+10]), 'kernel': ['rbf'], 'gamma': array([3.01995172e-07, 3.07609681e-07, 3.13328572e-07, 3.19153786e-07,
3.25087297e-07, 3.31131121e-07, 3.37287309e-07, 3.43557948e-07,
3.49945167e-07, 3.56451133e-07, 3.63078055e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}, 0.6947079314040729)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [9.99000000e+08 7.99424783e-08]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.00000000e+08, 1.58489319e+08, 2.51188643e+08, 3.98107171e+08,
6.30957344e+08, 1.00000000e+09, 1.58489319e+09, 2.51188643e+09,
3.98107171e+09, 6.30957344e+09, 1.00000000e+10]), 'kernel': ['rbf'], 'gamma': array([3.01995172e-07, 3.07609681e-07, 3.13328572e-07, 3.19153786e-07,
3.25087297e-07, 3.31131121e-07, 3.37287309e-07, 3.43557948e-07,
3.49945167e-07, 3.56451133e-07, 3.63078055e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.671 (+/-0.433) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.690 (+/-0.414) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.688 (+/-0.425) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.691 (+/-0.422) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.689 (+/-0.422) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.685 (+/-0.438) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.681 (+/-0.373) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.723 (+/-0.417) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.689 (+/-0.415) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.688 (+/-0.425) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.691 (+/-0.422) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.685 (+/-0.438) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.681 (+/-0.373) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.690 (+/-0.414) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.688 (+/-0.425) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.691 (+/-0.422) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.685 (+/-0.438) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.681 (+/-0.373) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.689 (+/-0.415) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.681 (+/-0.428) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.685 (+/-0.438) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.681 (+/-0.373) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.689 (+/-0.415) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.684 (+/-0.426) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.681 (+/-0.373) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.687 (+/-0.416) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.681 (+/-0.428) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.677 (+/-0.374) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.687 (+/-0.416) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.684 (+/-0.426) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.677 (+/-0.374) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.687 (+/-0.416) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.684 (+/-0.426) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.677 (+/-0.374) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.689 (+/-0.415) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.684 (+/-0.426) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.677 (+/-0.374) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.689 (+/-0.415) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.681 (+/-0.428) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.677 (+/-0.374) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.687 (+/-0.417) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.681 (+/-0.428) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.702 (+/-0.421) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.635 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.664 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.295) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.295) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.680 (+/-0.294) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6820497361695111
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.644 (+/-0.381) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.615 (+/-0.298) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.644 (+/-0.382) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.630 (+/-0.325) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.620 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.305) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.610 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.608 (+/-0.300) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.587 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.657 (+/-0.370) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.622 (+/-0.308) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.620 (+/-0.310) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.630 (+/-0.303) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.610 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.607 (+/-0.300) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.587 (+/-0.313) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.657 (+/-0.370) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.630 (+/-0.303) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.610 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.605 (+/-0.301) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.623 (+/-0.308) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.657 (+/-0.370) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.629 (+/-0.304) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.610 (+/-0.296) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.606 (+/-0.301) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.656 (+/-0.371) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.306) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.606 (+/-0.301) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.656 (+/-0.371) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.629 (+/-0.304) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.605 (+/-0.301) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.655 (+/-0.372) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.306) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.614 (+/-0.292) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.606 (+/-0.301) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.608 (+/-0.301) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.656 (+/-0.371) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.306) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.614 (+/-0.292) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.606 (+/-0.301) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.608 (+/-0.301) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.655 (+/-0.372) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.306) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.614 (+/-0.292) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.605 (+/-0.301) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.608 (+/-0.301) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.655 (+/-0.372) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.629 (+/-0.304) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.605 (+/-0.302) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.608 (+/-0.301) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.655 (+/-0.372) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.306) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.605 (+/-0.302) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.608 (+/-0.301) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.677 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.663 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.276) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.661 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.680 (+/-0.294) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.695 (+/-0.304) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.661 (+/-0.308) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.312) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.680 (+/-0.294) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.695 (+/-0.304) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.311) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.680 (+/-0.294) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.695 (+/-0.304) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.311) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.311) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.695 (+/-0.304) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.311) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.677 (+/-0.285) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.660 (+/-0.302) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.310) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.677 (+/-0.285) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.660 (+/-0.302) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.310) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.677 (+/-0.285) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.660 (+/-0.302) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.310) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.695 (+/-0.304) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.307) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.660 (+/-0.302) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.311) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.307) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.660 (+/-0.302) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.310) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.662 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6948687031082529
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.00000000e+08, 1.09647820e+08, 1.20226443e+08, 1.31825674e+08,
1.44543977e+08, 1.58489319e+08, 1.73780083e+08, 1.90546072e+08,
2.08929613e+08, 2.29086765e+08, 2.51188643e+08]), 'kernel': ['rbf'], 'gamma': array([3.25087297e-07, 3.26287172e-07, 3.27491476e-07, 3.28700224e-07,
3.29913434e-07, 3.31131121e-07, 3.32353304e-07, 3.33579997e-07,
3.34811217e-07, 3.36046982e-07, 3.37287309e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}, 0.6948687031082529)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [8.41510681e+08 0.00000000e+00]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.00000000e+08, 1.09647820e+08, 1.20226443e+08, 1.31825674e+08,
1.44543977e+08, 1.58489319e+08, 1.73780083e+08, 1.90546072e+08,
2.08929613e+08, 2.29086765e+08, 2.51188643e+08]), 'kernel': ['rbf'], 'gamma': array([3.25087297e-07, 3.26287172e-07, 3.27491476e-07, 3.28700224e-07,
3.29913434e-07, 3.31131121e-07, 3.32353304e-07, 3.33579997e-07,
3.34811217e-07, 3.36046982e-07, 3.37287309e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.689 (+/-0.422) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.689 (+/-0.422) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.743 (+/-0.433) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.739 (+/-0.437) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.743 (+/-0.433) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.689 (+/-0.422) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.743 (+/-0.433) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.693 (+/-0.419) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.689 (+/-0.422) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.693 (+/-0.419) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.689 (+/-0.422) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.693 (+/-0.419) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.689 (+/-0.422) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.743 (+/-0.433) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.689 (+/-0.422) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.743 (+/-0.433) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.719 (+/-0.399) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.635 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.295) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.665 (+/-0.316) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.657 (+/-0.216) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.657 (+/-0.215) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.215) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.657 (+/-0.216) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.665 (+/-0.316) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.295) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.215) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.657 (+/-0.216) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.295) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.215) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.295) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.215) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.657 (+/-0.216) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.657 (+/-0.216) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822099925797676
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.620 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.629 (+/-0.326) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.305) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.629 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.621 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.620 (+/-0.290) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.309) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.305) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.620 (+/-0.290) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.310) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.305) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.620 (+/-0.290) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.309) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.629 (+/-0.302) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.305) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.622 (+/-0.290) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.310) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.629 (+/-0.302) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.305) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.620 (+/-0.290) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.310) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.629 (+/-0.302) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.630 (+/-0.303) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.627 (+/-0.304) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.620 (+/-0.290) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.310) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.306) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.618 (+/-0.290) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.310) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.620 (+/-0.309) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.306) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.614 (+/-0.293) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.618 (+/-0.290) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.619 (+/-0.310) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.620 (+/-0.309) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.629 (+/-0.302) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.306) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.614 (+/-0.293) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.618 (+/-0.290) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.619 (+/-0.310) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.620 (+/-0.309) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.306) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.614 (+/-0.293) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.618 (+/-0.290) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.619 (+/-0.310) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.620 (+/-0.309) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.629 (+/-0.302) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.630 (+/-0.303) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.627 (+/-0.304) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.614 (+/-0.293) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.618 (+/-0.290) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.677 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.679 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.679 (+/-0.291) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.293) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.293) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.293) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.290) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.293) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.695 (+/-0.304) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.679 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.292) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.677 (+/-0.292) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.292) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.678 (+/-0.290) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.291) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.677 (+/-0.292) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.292) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.678 (+/-0.290) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.291) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.677 (+/-0.292) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.292) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.678 (+/-0.290) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.291) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.677 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.695 (+/-0.304) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.679 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.678 (+/-0.290) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.662 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6948687031082529
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.44543977e+08, 1.47231250e+08, 1.49968484e+08, 1.52756606e+08,
1.55596563e+08, 1.58489319e+08, 1.61435856e+08, 1.64437172e+08,
1.67494288e+08, 1.70608239e+08, 1.73780083e+08]), 'kernel': ['rbf'], 'gamma': array([3.29913434e-07, 3.30156613e-07, 3.30399971e-07, 3.30643508e-07,
3.30887225e-07, 3.31131121e-07, 3.31375198e-07, 3.31619454e-07,
3.31863890e-07, 3.32108507e-07, 3.32353304e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}, 0.6948687031082529)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [0. 0.]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 3.3113112148259127e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 158489319.24611127, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9355067328136074
测试集中，预测为舞弊样本的有： (array([ 56, 136, 370, 590, 769, 1017, 1122, 1246, 1247, 1248, 1251,
1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 14
训练模型SVC对测试样本的预测准确率： 0.9525159461374911
以上是第32次特征筛选。
第32次特征筛选，AUC值是： 0.8153728294177732
X_train_iter_svc.shape is: (1257, 20)
X_test_iter_svc.shape is: (1257, 20)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.649 (+/-0.386) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6826923076923077
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6413461538461539
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6826923076923077
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.319) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.798 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.317) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.798 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.317) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.823 (+/-0.451) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.651 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.631 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.661) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.768 (+/-0.475) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.731 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.660) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.607 (+/-0.395) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.706 (+/-0.383) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.641 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'linear'}
0.646 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.216) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.216) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.243) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.633 (+/-0.226) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.666 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.610 (+/-0.244) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.633 (+/-0.225) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.590 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6988782051282051
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.674 (+/-0.358) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.322) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.687 (+/-0.359) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.359) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.634 (+/-0.310) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.687 (+/-0.359) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.674 (+/-0.354) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.547 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.703 (+/-0.357) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.621 (+/-0.391) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.350) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.633 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.550 (+/-0.827) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.601 (+/-0.398) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.373) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.640 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.007) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.681 (+/-0.294) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.525 (+/-0.150) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.610 (+/-0.242) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.612 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.610 (+/-0.244) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.592 (+/-0.228) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.583 (+/-0.305) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.656 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.683 (+/-0.294) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.12 0.17 0.14 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982361488993323
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.823 (+/-0.451) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.752 (+/-0.430) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.698 (+/-0.383) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.373) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.848 (+/-0.459) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.739 (+/-0.452) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.726 (+/-0.394) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.673 (+/-0.330) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.326) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.648 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.848 (+/-0.460) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.706 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.773 (+/-0.417) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.723 (+/-0.358) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.326) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.652 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.798 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.748 (+/-0.389) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.380) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.691 (+/-0.357) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.371) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.704 (+/-0.353) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.373) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.652 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.647 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.773 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.731 (+/-0.394) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.373) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.715 (+/-0.368) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.675 (+/-0.375) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.668 (+/-0.370) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.647 (+/-0.341) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.327) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.642 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.768 (+/-0.475) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.355) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.675 (+/-0.431) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.731 (+/-0.401) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.677 (+/-0.374) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.642 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.473) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.355) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.379) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.731 (+/-0.400) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.371) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.328) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.473) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.355) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.671 (+/-0.377) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.689 (+/-0.382) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.633 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.371) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.328) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.296) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.614 (+/-0.397) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.764 (+/-0.478) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.355) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.673 (+/-0.377) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.714 (+/-0.360) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.634 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.633 (+/-0.298) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.673 (+/-0.376) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.296) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.449) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.739 (+/-0.452) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.352) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.664 (+/-0.382) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.706 (+/-0.383) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.628 (+/-0.299) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.633 (+/-0.298) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.328) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.607 (+/-0.395) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.764 (+/-0.478) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.352) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.673 (+/-0.375) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.706 (+/-0.383) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.373) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.633 (+/-0.298) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.653 (+/-0.329) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.653 (+/-0.297) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'linear'}
0.646 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.699 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.658 (+/-0.217) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.700 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.699 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.700 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.699 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.667 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.683 (+/-0.296) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.665 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.698 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.666 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.664 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.699 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.640 (+/-0.326) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.666 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.664 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.699 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.323) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.295) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.323) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.696 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.697 (+/-0.306) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.295) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.298) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.323) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.299) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.697 (+/-0.306) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.299) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6996794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.814 (+/-0.459) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.673 (+/-0.370) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.703 (+/-0.357) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.657 (+/-0.358) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.687 (+/-0.358) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.647 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.629 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.630 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.706 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.643 (+/-0.372) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.651 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.683 (+/-0.354) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.637 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.632 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.627 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.739 (+/-0.452) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.714 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.677 (+/-0.350) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.638 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.289) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.679 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.637 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.631 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.372) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.714 (+/-0.418) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.706 (+/-0.418) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.669 (+/-0.353) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.616 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.653 (+/-0.287) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.299) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.688 (+/-0.357) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.634 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.323) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.624 (+/-0.389) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.706 (+/-0.418) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.731 (+/-0.394) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.354) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.611 (+/-0.279) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.634 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.680 (+/-0.355) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.631 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.639 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.621 (+/-0.391) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.689 (+/-0.382) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.719 (+/-0.399) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.657 (+/-0.358) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.600 (+/-0.287) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.350) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.375) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.630 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.323) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.620 (+/-0.391) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.673 (+/-0.370) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.706 (+/-0.404) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.643 (+/-0.365) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.608 (+/-0.279) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.349) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.300) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.376) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.625 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.639 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.612 (+/-0.391) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.673 (+/-0.370) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.680 (+/-0.358) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.643 (+/-0.365) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.614 (+/-0.279) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.299) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.291) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.376) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.639 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.397) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.668 (+/-0.367) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.676 (+/-0.360) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.613 (+/-0.292) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.611 (+/-0.280) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.618 (+/-0.300) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.300) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.640 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.398) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.660 (+/-0.367) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.675 (+/-0.361) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.638 (+/-0.367) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.600 (+/-0.278) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.378) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.613 (+/-0.289) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.300) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.633 (+/-0.306) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.640 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.398) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.664 (+/-0.366) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.674 (+/-0.362) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.613 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.610 (+/-0.277) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.678 (+/-0.375) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.650 (+/-0.371) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.623 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.627 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.640 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.665 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.679 (+/-0.288) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.663 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.680 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.698 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.679 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.665 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.291) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.614 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.682 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.680 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.680 (+/-0.293) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.612 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.682 (+/-0.295) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.680 (+/-0.288) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.661 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.612 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.665 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.682 (+/-0.295) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.679 (+/-0.286) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.677 (+/-0.291) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.295) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.610 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.665 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.681 (+/-0.295) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.679 (+/-0.286) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.694 (+/-0.304) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.294) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.306) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.665 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.681 (+/-0.295) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.678 (+/-0.286) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.693 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.295) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.294) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.305) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.665 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.681 (+/-0.295) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.678 (+/-0.286) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.676 (+/-0.290) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.297) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.305) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.665 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.681 (+/-0.295) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.678 (+/-0.286) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.693 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.12 0.17 0.14 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982361488993323
循环迭代之前，delta is: [8.41510681e+06 2.11758237e-22]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1000000. , 1096478.19614319, 1202264.43461741,
1318256.73855641, 1445439.77074593, 1584893.19246111,
1737800.82874938, 1905460.71796325, 2089296.13085404,
2290867.65276777, 2511886.43150958]), 'kernel': ['rbf'], 'gamma': array([6.30957344e-07, 6.91830971e-07, 7.58577575e-07, 8.31763771e-07,
9.12010839e-07, 1.00000000e-06, 1.09647820e-06, 1.20226443e-06,
1.31825674e-06, 1.44543977e-06, 1.58489319e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.823 (+/-0.451) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.798 (+/-0.437) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.747 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.798 (+/-0.492) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.773 (+/-0.474) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.748 (+/-0.449) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.773 (+/-0.474) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.748 (+/-0.449) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.798 (+/-0.437) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.748 (+/-0.449) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.748 (+/-0.449) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.773 (+/-0.474) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.773 (+/-0.474) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.773 (+/-0.474) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.748 (+/-0.449) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.739 (+/-0.452) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.723 (+/-0.417) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.748 (+/-0.449) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.748 (+/-0.449) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.723 (+/-0.417) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.773 (+/-0.416) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.748 (+/-0.449) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.748 (+/-0.449) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.748 (+/-0.449) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.723 (+/-0.417) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.773 (+/-0.416) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.748 (+/-0.449) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.723 (+/-0.417) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.452) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.723 (+/-0.417) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.773 (+/-0.416) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.723 (+/-0.417) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.748 (+/-0.389) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.739 (+/-0.452) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.452) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.714 (+/-0.418) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.739 (+/-0.392) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.739 (+/-0.392) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.714 (+/-0.418) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.706 (+/-0.418) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.452) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.723 (+/-0.417) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.773 (+/-0.417) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.740 (+/-0.392) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.352) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.731 (+/-0.394) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.714 (+/-0.418) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.691 (+/-0.422) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.452) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.723 (+/-0.417) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.773 (+/-0.417) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.740 (+/-0.392) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.352) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.740 (+/-0.392) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.698 (+/-0.423) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.674 (+/-0.383) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.714 (+/-0.418) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.739 (+/-0.452) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.714 (+/-0.418) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.760 (+/-0.426) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.773 (+/-0.417) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.740 (+/-0.392) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.706 (+/-0.353) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.740 (+/-0.392) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.689 (+/-0.422) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.674 (+/-0.383) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.706 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.739 (+/-0.452) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.714 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.760 (+/-0.426) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.773 (+/-0.417) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.731 (+/-0.394) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.706 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.701 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.668 (+/-0.371) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.683 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'linear'}
0.646 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.641 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.667 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.667 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.667 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.296) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.667 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.667 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.317) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.317) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.317) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.315) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.699 (+/-0.308) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.682 (+/-0.296) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.682 (+/-0.296) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.317) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.699 (+/-0.308) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.682 (+/-0.296) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.699 (+/-0.308) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.314) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.699 (+/-0.308) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.682 (+/-0.295) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.699 (+/-0.308) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.314) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.699 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.682 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.698 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.681 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6996794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.673 (+/-0.370) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.723 (+/-0.417) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.689 (+/-0.352) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.684 (+/-0.355) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.703 (+/-0.357) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.714 (+/-0.399) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.636 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.624 (+/-0.288) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.645 (+/-0.323) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.657 (+/-0.358) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.668 (+/-0.367) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.723 (+/-0.417) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.689 (+/-0.352) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.679 (+/-0.356) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.690 (+/-0.354) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.704 (+/-0.405) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.632 (+/-0.287) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.624 (+/-0.288) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.637 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.632 (+/-0.288) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.368) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.714 (+/-0.418) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.698 (+/-0.383) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.674 (+/-0.358) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.675 (+/-0.359) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.691 (+/-0.357) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.410) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.631 (+/-0.287) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.627 (+/-0.287) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.641 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.632 (+/-0.288) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.367) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.380) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.683 (+/-0.386) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.655 (+/-0.358) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.664 (+/-0.357) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.656 (+/-0.292) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.674 (+/-0.359) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.629 (+/-0.288) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.634 (+/-0.287) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.632 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.637 (+/-0.288) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.644 (+/-0.371) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.674 (+/-0.383) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.653 (+/-0.376) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.644 (+/-0.363) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.356) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.651 (+/-0.293) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.674 (+/-0.359) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.629 (+/-0.288) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.634 (+/-0.286) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.632 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.641 (+/-0.288) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.643 (+/-0.372) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.659 (+/-0.375) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.626 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.608 (+/-0.298) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.662 (+/-0.355) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.651 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.674 (+/-0.359) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.631 (+/-0.288) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.634 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.636 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.642 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.643 (+/-0.372) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.654 (+/-0.371) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.621 (+/-0.303) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.608 (+/-0.298) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.660 (+/-0.357) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.646 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.676 (+/-0.358) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.631 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.634 (+/-0.286) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.626 (+/-0.292) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.641 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.641 (+/-0.373) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.654 (+/-0.371) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.615 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.609 (+/-0.298) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.662 (+/-0.356) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.658 (+/-0.292) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.679 (+/-0.357) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.632 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.622 (+/-0.276) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.631 (+/-0.299) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.624 (+/-0.300) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.639 (+/-0.374) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.624 (+/-0.299) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.612 (+/-0.298) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.609 (+/-0.298) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.660 (+/-0.359) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.651 (+/-0.293) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.355) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.614 (+/-0.289) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.629 (+/-0.275) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.631 (+/-0.299) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.624 (+/-0.300) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.638 (+/-0.375) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.620 (+/-0.299) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.612 (+/-0.298) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.610 (+/-0.298) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.661 (+/-0.358) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.653 (+/-0.287) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.355) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.639 (+/-0.287) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.636 (+/-0.278) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.631 (+/-0.299) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.624 (+/-0.301) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.638 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.620 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.627 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.610 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.656 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.643 (+/-0.287) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.636 (+/-0.278) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.631 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.640 (+/-0.289) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.665 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.682 (+/-0.294) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.698 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.679 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.679 (+/-0.288) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.314) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.317) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.682 (+/-0.294) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.697 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.291) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.679 (+/-0.290) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.679 (+/-0.288) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.314) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.665 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.681 (+/-0.293) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.697 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.290) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.679 (+/-0.290) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.679 (+/-0.288) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.665 (+/-0.315) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.680 (+/-0.292) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.696 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.679 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.680 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.314) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.680 (+/-0.288) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.315) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.664 (+/-0.314) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.679 (+/-0.290) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.697 (+/-0.302) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.679 (+/-0.289) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.680 (+/-0.291) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.314) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.680 (+/-0.289) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.663 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.697 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.679 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.680 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.680 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.313) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.314) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.663 (+/-0.314) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.310) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.696 (+/-0.302) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.679 (+/-0.289) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.680 (+/-0.292) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.314) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.680 (+/-0.289) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.314) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.662 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.309) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.696 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.306) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.289) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.679 (+/-0.291) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.663 (+/-0.309) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.313) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.312) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.662 (+/-0.312) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.308) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.680 (+/-0.291) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.663 (+/-0.308) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.680 (+/-0.292) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.663 (+/-0.309) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.313) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.311) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.662 (+/-0.312) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.308) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.680 (+/-0.291) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.288) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.697 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.663 (+/-0.308) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.679 (+/-0.291) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.697 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.697 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.679 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1202264.43461741, 1224616.19926505, 1247383.51424294,
1270574.10520854, 1294195.84144999, 1318256.73855641,
1342764.96113787, 1367728.82559585, 1393156.8029453 ,
1419057.52168909, 1445439.77074593]), 'kernel': ['rbf'], 'gamma': array([9.12010839e-07, 9.28966387e-07, 9.46237161e-07, 9.63829024e-07,
9.81747943e-07, 1.00000000e-06, 1.01859139e-06, 1.03752842e-06,
1.05681751e-06, 1.07646521e-06, 1.09647820e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}, 0.6996794871794872)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [266636.45390471 0. ]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1202264.43461741, 1224616.19926505, 1247383.51424294,
1270574.10520854, 1294195.84144999, 1318256.73855641,
1342764.96113787, 1367728.82559585, 1393156.8029453 ,
1419057.52168909, 1445439.77074593]), 'kernel': ['rbf'], 'gamma': array([9.12010839e-07, 9.28966387e-07, 9.46237161e-07, 9.63829024e-07,
9.81747943e-07, 1.00000000e-06, 1.01859139e-06, 1.03752842e-06,
1.05681751e-06, 1.07646521e-06, 1.09647820e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.739 (+/-0.452) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.748 (+/-0.449) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.748 (+/-0.449) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.764 (+/-0.421) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.739 (+/-0.452) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.764 (+/-0.421) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.739 (+/-0.452) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.764 (+/-0.421) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.723 (+/-0.417) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.739 (+/-0.392) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.798 (+/-0.437) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.723 (+/-0.417) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.739 (+/-0.392) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.798 (+/-0.437) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.731 (+/-0.394) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.781 (+/-0.417) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.773 (+/-0.416) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.731 (+/-0.394) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.781 (+/-0.417) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.723 (+/-0.417) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.714 (+/-0.360) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.756 (+/-0.391) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.723 (+/-0.417) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.748 (+/-0.449) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.714 (+/-0.360) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.731 (+/-0.394) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.756 (+/-0.391) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.723 (+/-0.417) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.748 (+/-0.449) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.714 (+/-0.360) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.731 (+/-0.394) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.748 (+/-0.388) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.723 (+/-0.417) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.714 (+/-0.360) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.731 (+/-0.394) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.773 (+/-0.416) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'linear'}
0.646 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.295) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.296) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.295) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.295) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.295) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.295) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.682 (+/-0.296) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.295) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.295) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.682 (+/-0.296) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.294) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.295) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.682 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6996794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.675 (+/-0.359) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.659 (+/-0.359) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.660 (+/-0.357) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.663 (+/-0.357) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.670 (+/-0.353) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.691 (+/-0.357) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.354) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.640 (+/-0.299) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.669 (+/-0.363) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.693 (+/-0.412) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.695 (+/-0.410) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.667 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.659 (+/-0.359) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.660 (+/-0.357) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.659 (+/-0.358) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.662 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.681 (+/-0.355) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.648 (+/-0.315) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.669 (+/-0.363) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.693 (+/-0.412) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.668 (+/-0.355) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.659 (+/-0.359) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.660 (+/-0.357) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.654 (+/-0.360) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.664 (+/-0.355) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.689 (+/-0.358) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.357) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.635 (+/-0.293) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.669 (+/-0.363) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.693 (+/-0.412) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.668 (+/-0.355) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.652 (+/-0.361) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.658 (+/-0.358) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.654 (+/-0.360) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.664 (+/-0.355) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.681 (+/-0.355) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.357) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.665 (+/-0.366) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.669 (+/-0.363) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.668 (+/-0.362) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.357) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.652 (+/-0.361) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.656 (+/-0.358) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.654 (+/-0.360) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.664 (+/-0.355) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.292) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.634 (+/-0.288) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.665 (+/-0.366) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.669 (+/-0.363) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.668 (+/-0.362) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.357) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.651 (+/-0.362) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.656 (+/-0.358) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.652 (+/-0.362) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.662 (+/-0.356) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.292) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.287) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.635 (+/-0.293) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.668 (+/-0.364) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.672 (+/-0.361) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.357) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.359) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.656 (+/-0.358) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.651 (+/-0.363) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.355) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.681 (+/-0.355) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.637 (+/-0.288) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.665 (+/-0.366) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.668 (+/-0.364) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.668 (+/-0.362) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.357) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.359) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.656 (+/-0.358) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.651 (+/-0.363) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.355) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.651 (+/-0.293) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.290) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.665 (+/-0.366) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.673 (+/-0.362) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.670 (+/-0.362) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.667 (+/-0.354) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.359) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.656 (+/-0.358) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.651 (+/-0.363) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.355) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.651 (+/-0.293) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.290) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.648 (+/-0.315) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.673 (+/-0.362) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.670 (+/-0.362) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.665 (+/-0.356) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.359) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.657 (+/-0.357) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.655 (+/-0.362) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.355) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.651 (+/-0.293) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.637 (+/-0.288) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.648 (+/-0.315) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.673 (+/-0.362) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.675 (+/-0.360) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.665 (+/-0.356) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.359) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.657 (+/-0.357) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.655 (+/-0.362) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.355) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.651 (+/-0.293) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.290) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.650 (+/-0.314) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.673 (+/-0.362) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.672 (+/-0.361) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.697 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.697 (+/-0.303) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.696 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.697 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.302) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.697 (+/-0.303) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.306) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.696 (+/-0.305) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.302) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.290) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.696 (+/-0.302) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.290) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.306) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.306) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.696 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.679 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.696 (+/-0.305) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.696 (+/-0.302) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.306) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.306) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.696 (+/-0.302) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.305) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.303) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.305) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.302) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.305) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.302) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.11 0.17 0.13 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6979146054909721
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1224616.19926505, 1229136.17306017, 1233672.82976633,
1238226.23095899, 1242796.43844091, 1247383.51424294,
1251987.52062488, 1256608.52007633, 1261246.57531754,
1265901.74930024, 1270574.10520854]), 'kernel': ['rbf'], 'gamma': array([9.81747943e-07, 9.85371507e-07, 9.89008445e-07, 9.92658807e-07,
9.96322642e-07, 1.00000000e-06, 1.00369093e-06, 1.00739548e-06,
1.01111371e-06, 1.01484566e-06, 1.01859139e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}, 0.6996794871794872)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [70873.22431346 0. ]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1224616.19926505, 1229136.17306017, 1233672.82976633,
1238226.23095899, 1242796.43844091, 1247383.51424294,
1251987.52062488, 1256608.52007633, 1261246.57531754,
1265901.74930024, 1270574.10520854]), 'kernel': ['rbf'], 'gamma': array([9.81747943e-07, 9.85371507e-07, 9.89008445e-07, 9.92658807e-07,
9.96322642e-07, 1.00000000e-06, 1.00369093e-06, 1.00739548e-06,
1.01111371e-06, 1.01484566e-06, 1.01859139e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.723 (+/-0.417) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'linear'}
0.646 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.683 (+/-0.296) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.683 (+/-0.296) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.683 (+/-0.296) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6996794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.662 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.665 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.676 (+/-0.357) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.664 (+/-0.355) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.664 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.662 (+/-0.356) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.663 (+/-0.357) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.665 (+/-0.356) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.676 (+/-0.357) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.664 (+/-0.355) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.662 (+/-0.357) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.662 (+/-0.356) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.663 (+/-0.357) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.665 (+/-0.356) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.676 (+/-0.357) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.677 (+/-0.358) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.664 (+/-0.355) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.665 (+/-0.357) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.662 (+/-0.357) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.662 (+/-0.356) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.676 (+/-0.357) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.675 (+/-0.358) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.665 (+/-0.357) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.662 (+/-0.357) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.675 (+/-0.358) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.662 (+/-0.357) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.689 (+/-0.358) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.638 (+/-0.290) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.661 (+/-0.358) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.647 (+/-0.294) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.652 (+/-0.294) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.676 (+/-0.357) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.358) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.358) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.306) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.304) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.306) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.305) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.306) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.305) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.306) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.11 0.17 0.13 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.697753833786792
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1238226.23095899, 1239138.92597054, 1240052.29372844,
1240966.33472858, 1241881.0494672 , 1242796.43844091,
1243712.5021467 , 1244629.2410819 , 1245546.65574423,
1246464.74663176, 1247383.51424294]), 'kernel': ['rbf'], 'gamma': array([9.96322642e-07, 9.97057030e-07, 9.97791960e-07, 9.98527431e-07,
9.99263444e-07, 1.00000000e-06, 1.00073710e-06, 1.00147474e-06,
1.00221293e-06, 1.00295166e-06, 1.00369093e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}, 0.6996794871794872)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [4587.07580203 0. ]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 1.0000000000000002e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 1242796.4384409143, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9461374911410347
测试集中，预测为舞弊样本的有： (array([ 8, 10, 11, 12, 13, 14, 21, 22, 24, 25, 26,
27, 28, 30, 33, 34, 35, 36, 37, 39, 40, 42,
43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 56,
58, 59, 60, 61, 62, 64, 65, 66, 67, 68, 69,
70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 82,
88, 90, 91, 97, 98, 101, 102, 109, 111, 112, 113,
114, 115, 116, 117, 118, 119, 121, 127, 129, 136, 142,
143, 144, 145, 146, 147, 148, 150, 151, 153, 157, 158,
163, 164, 165, 166, 176, 177, 178, 179, 180, 181, 182,
183, 184, 185, 186, 192, 193, 195, 197, 200, 201, 202,
205, 207, 208, 209, 211, 212, 213, 214, 215, 216, 219,
225, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238,
239, 243, 244, 245, 246, 247, 248, 250, 251, 252, 253,
254, 255, 256, 257, 258, 259, 260, 261, 264, 268, 269,
270, 276, 277, 279, 281, 284, 286, 287, 288, 289, 300,
301, 302, 306, 316, 318, 322, 323, 324, 325, 330, 331,
332, 333, 338, 339, 340, 342, 344, 346, 347, 348, 349,
351, 352, 353, 354, 358, 359, 360, 361, 362, 363, 364,
365, 366, 370, 383, 384, 385, 386, 391, 392, 393, 394,
395, 396, 397, 398, 399, 403, 404, 414, 419, 420, 421,
422, 423, 426, 427, 428, 429, 430, 431, 432, 433, 434,
438, 439, 440, 441, 442, 443, 444, 445, 447, 449, 450,
454, 455, 457, 458, 459, 460, 461, 462, 463, 464, 465,
466, 467, 468, 469, 471, 472, 473, 474, 475, 476, 477,
478, 479, 481, 482, 483, 484, 485, 486, 487, 488, 489,
490, 491, 495, 496, 498, 499, 500, 501, 502, 503, 504,
505, 506, 507, 508, 509, 511, 513, 515, 516, 521, 529,
532, 534, 535, 540, 543, 544, 545, 546, 547, 549, 555,
558, 560, 561, 562, 563, 564, 565, 566, 567, 568, 571,
572, 573, 584, 587, 589, 590, 593, 604, 605, 611, 614,
615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 629,
630, 635, 641, 642, 643, 651, 652, 653, 655, 656, 657,
658, 659, 660, 661, 662, 666, 667, 668, 669, 675, 676,
677, 678, 685, 686, 687, 688, 693, 694, 695, 696, 697,
699, 701, 702, 703, 706, 709, 716, 717, 718, 719, 720,
721, 722, 723, 724, 725, 729, 735, 736, 742, 743, 750,
751, 752, 753, 754, 756, 757, 758, 759, 760, 761, 769,
774, 775, 776, 777, 778, 786, 787, 788, 789, 790, 791,
792, 793, 794, 795, 796, 797, 813, 814, 816, 817, 818,
819, 820, 825, 826, 829, 830, 840, 841, 845, 851, 852,
853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 864,
872, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883,
884, 885, 897, 898, 900, 902, 903, 904, 905, 906, 907,
910, 911, 912, 913, 914, 917, 919, 927, 928, 929, 930,
931, 932, 933, 934, 935, 947, 951, 952, 953, 954, 955,
956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966,
967, 977, 978, 979, 980, 981, 982, 983, 984, 985, 995,
998, 999, 1000, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009,
1012, 1013, 1014, 1015, 1016, 1017, 1020, 1021, 1022, 1030, 1037,
1041, 1049, 1050, 1051, 1055, 1056, 1057, 1060, 1061, 1065, 1067,
1068, 1074, 1080, 1081, 1082, 1085, 1086, 1089, 1095, 1096, 1097,
1098, 1105, 1106, 1107, 1110, 1112, 1113, 1114, 1117, 1118, 1119,
1121, 1122, 1123, 1124, 1125, 1127, 1129, 1131, 1132, 1134, 1135,
1136, 1137, 1140, 1141, 1142, 1144, 1148, 1152, 1153, 1154, 1156,
1158, 1160, 1164, 1167, 1168, 1170, 1171, 1175, 1180, 1181, 1183,
1184, 1188, 1189, 1191, 1194, 1200, 1204, 1205, 1208, 1211, 1215,
1216, 1217, 1218, 1219, 1222, 1230, 1232, 1233, 1234, 1235, 1236,
1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1246, 1247, 1248,
1249, 1250, 1251, 1252, 1254, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 645
训练模型SVC对测试样本的预测准确率： 0.5393338058114813
以上是第33次特征筛选。
第33次特征筛选，AUC值是： 0.6997300452356633
X_train_iter_svc.shape is: (1257, 19)
X_test_iter_svc.shape is: (1257, 19)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.647 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.656 (+/-0.218) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.682 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6817292233489982
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6979151207848958
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6817292233489982
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6979151207848958
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.797 (+/-0.492) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.665 (+/-0.225) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.319) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.218) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.312) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.645 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.218) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.310) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.218) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.299) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.460) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.651 (+/-0.211) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.690 (+/-0.299) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.823 (+/-0.451) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.685 (+/-0.297) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.290) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.474) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.601 (+/-0.176) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.701 (+/-0.417) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.413) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.294) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.642 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.294) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.294) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.667 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.674 (+/-0.324) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.616 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985566617198451
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.582 (+/-0.184) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.671 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.645 (+/-0.386) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.680 (+/-0.295) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.185) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.322) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.165) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.645 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.601 (+/-0.159) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.302) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.604 (+/-0.159) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.302) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.627 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.607 (+/-0.170) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.739 (+/-0.392) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.634 (+/-0.288) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.646 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.724 (+/-0.786) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.582 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.591 (+/-0.149) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.623 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.436 (+/-0.582) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.575 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.620 (+/-0.184) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.622 (+/-0.317) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.295) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.007) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.696 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.653 (+/-0.313) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.696 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.586 (+/-0.235) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.695 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.678 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.589 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.304) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.335) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.584 (+/-0.300) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982351183114849
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.848 (+/-0.459) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.001}
0.715 (+/-0.319) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.690 (+/-0.300) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.690 (+/-0.300) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.690 (+/-0.300) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.656 (+/-0.218) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.673 (+/-0.294) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.294) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.673 (+/-0.294) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.681 (+/-0.297) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.656 (+/-0.312) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.1}
0.715 (+/-0.319) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.300) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.690 (+/-0.300) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.690 (+/-0.300) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.656 (+/-0.218) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.673 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.673 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.297) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.706 (+/-0.353) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.690 (+/-0.300) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.300) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.690 (+/-0.300) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.656 (+/-0.218) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.673 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.673 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.673 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.681 (+/-0.297) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.706 (+/-0.353) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.631 (+/-0.319) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.624 (+/-0.318) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.690 (+/-0.300) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.300) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.656 (+/-0.218) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.673 (+/-0.294) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.673 (+/-0.294) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.673 (+/-0.294) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.681 (+/-0.297) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.310) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.373) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.626 (+/-0.318) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.622 (+/-0.319) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.1}
0.690 (+/-0.300) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.218) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.673 (+/-0.294) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.648 (+/-0.210) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.673 (+/-0.294) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.690 (+/-0.299) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.310) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.352) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.643 (+/-0.306) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.622 (+/-0.319) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.218) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.294) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.648 (+/-0.210) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.673 (+/-0.294) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.681 (+/-0.297) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.310) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.352) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.656 (+/-0.312) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.622 (+/-0.319) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.1}
0.673 (+/-0.294) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.001}
0.648 (+/-0.210) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.673 (+/-0.294) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.681 (+/-0.297) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.310) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.352) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.656 (+/-0.312) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.637 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.620 (+/-0.320) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.1}
0.648 (+/-0.210) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.294) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.681 (+/-0.297) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.310) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.352) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.656 (+/-0.312) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.649 (+/-0.314) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.327) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.620 (+/-0.320) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.1}
0.673 (+/-0.294) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.297) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.690 (+/-0.299) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.352) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.326) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.644 (+/-0.309) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.643 (+/-0.310) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.327) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.619 (+/-0.321) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.1}
0.681 (+/-0.297) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.299) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.352) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.326) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.649 (+/-0.314) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.643 (+/-0.310) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.626 (+/-0.318) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.327) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.621 (+/-0.320) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.1}
0.690 (+/-0.299) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.352) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.664 (+/-0.326) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.649 (+/-0.314) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.646 (+/-0.317) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.639 (+/-0.314) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.626 (+/-0.318) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.327) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.622 (+/-0.319) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.294) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.658 (+/-0.217) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.308) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.699 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.699 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.699 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.682 (+/-0.294) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.682 (+/-0.294) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.632 (+/-0.225) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.1}
0.699 (+/-0.308) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.699 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.699 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.682 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.682 (+/-0.296) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.615 (+/-0.237) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.699 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.699 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.682 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.296) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.615 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.699 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.682 (+/-0.294) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.294) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.699 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.316) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.1}
0.699 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.294) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.699 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.699 (+/-0.308) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.296) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.640 (+/-0.235) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.237) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.294) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.307) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.307) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.307) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.699 (+/-0.308) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.296) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.237) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.233) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.699 (+/-0.308) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.682 (+/-0.296) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.315) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.639 (+/-0.235) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.307) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.699 (+/-0.308) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.682 (+/-0.296) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.316) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.316) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.588 (+/-0.232) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.234) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.233) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.307) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.682 (+/-0.296) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.316) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.315) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.663 (+/-0.316) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.587 (+/-0.234) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.233) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.682 (+/-0.296) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.316) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.316) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.663 (+/-0.316) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.612 (+/-0.242) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.587 (+/-0.233) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.234) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.296) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.316) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.316) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.663 (+/-0.317) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.662 (+/-0.316) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.612 (+/-0.242) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.587 (+/-0.234) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.234) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6990374309506142
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.680 (+/-0.295) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.225) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.660 (+/-0.219) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.660 (+/-0.219) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.640 (+/-0.199) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.605 (+/-0.165) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.643 (+/-0.285) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.646 (+/-0.284) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.656 (+/-0.291) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.650 (+/-0.296) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.643 (+/-0.306) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.225) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.219) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.660 (+/-0.219) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.640 (+/-0.199) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.605 (+/-0.165) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.647 (+/-0.285) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.646 (+/-0.284) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.656 (+/-0.291) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.651 (+/-0.295) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.649 (+/-0.298) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.619 (+/-0.322) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.219) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.219) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.640 (+/-0.199) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.601 (+/-0.159) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.647 (+/-0.285) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.646 (+/-0.284) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.656 (+/-0.291) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.649 (+/-0.296) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.647 (+/-0.298) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.628 (+/-0.313) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.618 (+/-0.322) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.219) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.199) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.601 (+/-0.159) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.647 (+/-0.285) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.650 (+/-0.284) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.290) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.649 (+/-0.296) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.656 (+/-0.304) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.628 (+/-0.313) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.622 (+/-0.319) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.328) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.1}
0.640 (+/-0.199) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.601 (+/-0.159) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.647 (+/-0.285) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.625 (+/-0.184) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.658 (+/-0.290) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.302) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.658 (+/-0.302) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.645 (+/-0.301) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.625 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.618 (+/-0.322) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.601 (+/-0.159) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.001}
0.647 (+/-0.285) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.623 (+/-0.183) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.658 (+/-0.290) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.658 (+/-0.302) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.302) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.645 (+/-0.301) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.626 (+/-0.314) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.621 (+/-0.320) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.619 (+/-0.322) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.285) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.001}
0.623 (+/-0.183) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.658 (+/-0.290) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.658 (+/-0.302) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.658 (+/-0.302) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.645 (+/-0.301) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.626 (+/-0.314) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.626 (+/-0.314) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.623 (+/-0.319) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.620 (+/-0.320) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.1}
0.623 (+/-0.183) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.290) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.658 (+/-0.302) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.658 (+/-0.302) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.645 (+/-0.301) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.625 (+/-0.314) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.626 (+/-0.314) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.622 (+/-0.319) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.619 (+/-0.322) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.620 (+/-0.320) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.1}
0.658 (+/-0.290) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.302) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.658 (+/-0.302) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.645 (+/-0.301) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.625 (+/-0.314) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.626 (+/-0.314) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.625 (+/-0.315) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.623 (+/-0.319) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.619 (+/-0.321) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.1}
0.658 (+/-0.302) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.302) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.646 (+/-0.301) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.625 (+/-0.314) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.626 (+/-0.314) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.625 (+/-0.315) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.623 (+/-0.319) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.618 (+/-0.322) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.621 (+/-0.320) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.1}
0.658 (+/-0.302) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.301) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.625 (+/-0.314) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.626 (+/-0.314) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.625 (+/-0.315) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.627 (+/-0.313) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.622 (+/-0.319) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.327) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.622 (+/-0.319) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.295) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.306) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.306) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.306) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.305) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.696 (+/-0.303) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.304) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.305) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.305) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.680 (+/-0.292) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.653 (+/-0.313) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.306) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.305) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.696 (+/-0.303) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.304) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.305) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.305) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.680 (+/-0.293) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.678 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.244) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.306) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.305) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.696 (+/-0.303) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.697 (+/-0.304) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.305) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.305) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.679 (+/-0.292) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.678 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.659 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.243) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.306) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.305) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.696 (+/-0.303) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.697 (+/-0.305) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.697 (+/-0.305) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.306) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.679 (+/-0.292) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.695 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.660 (+/-0.315) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.634 (+/-0.327) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.585 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.305) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.696 (+/-0.303) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.697 (+/-0.305) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.697 (+/-0.305) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.697 (+/-0.306) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.696 (+/-0.305) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.695 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.676 (+/-0.296) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.659 (+/-0.313) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.609 (+/-0.243) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.585 (+/-0.237) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.696 (+/-0.303) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.305) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.697 (+/-0.305) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.697 (+/-0.306) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.696 (+/-0.305) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.695 (+/-0.307) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.676 (+/-0.296) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.659 (+/-0.313) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.634 (+/-0.326) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.610 (+/-0.242) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.586 (+/-0.235) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.305) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.305) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.697 (+/-0.306) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.696 (+/-0.305) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.695 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.676 (+/-0.296) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.659 (+/-0.313) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.659 (+/-0.313) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.635 (+/-0.326) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.585 (+/-0.236) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.305) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.306) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.696 (+/-0.305) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.695 (+/-0.307) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.676 (+/-0.296) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.659 (+/-0.313) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.659 (+/-0.314) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.634 (+/-0.326) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.610 (+/-0.242) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.306) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.001}
0.696 (+/-0.305) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.695 (+/-0.307) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.676 (+/-0.296) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.659 (+/-0.313) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.659 (+/-0.314) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.659 (+/-0.313) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.635 (+/-0.326) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.586 (+/-0.235) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.1}
0.696 (+/-0.305) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.676 (+/-0.296) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.659 (+/-0.313) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.659 (+/-0.313) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.659 (+/-0.312) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.635 (+/-0.326) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.611 (+/-0.241) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.235) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.1}
0.695 (+/-0.307) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.001}
0.676 (+/-0.296) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.659 (+/-0.312) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.659 (+/-0.313) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.659 (+/-0.312) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.660 (+/-0.314) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.636 (+/-0.324) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.586 (+/-0.235) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.234) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
发现最优参数C为原先的最大/最小值，直接重新设置超参。
循环迭代之前，delta is: [9.00000000e+02 8.41510681e-03]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04,
1.e+05, 1.e+06, 1.e+07]), 'kernel': ['rbf'], 'gamma': array([0.001 , 0.00109648, 0.00120226, 0.00131826, 0.00144544,
0.00158489, 0.0017378 , 0.00190546, 0.0020893 , 0.00229087,
0.00251189])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.823 (+/-0.451) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.823 (+/-0.451) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.748 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.715 (+/-0.319) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.698 (+/-0.319) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.693 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.685 (+/-0.297) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.685 (+/-0.297) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.690 (+/-0.300) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.656 (+/-0.218) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.297) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.681 (+/-0.297) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.673 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.673 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.673 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.648 (+/-0.210) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.648 (+/-0.210) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.648 (+/-0.210) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.648 (+/-0.210) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.648 (+/-0.210) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.690 (+/-0.299) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.690 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.698 (+/-0.352) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.698 (+/-0.352) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.698 (+/-0.352) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.681 (+/-0.373) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.681 (+/-0.373) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.681 (+/-0.373) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.681 (+/-0.373) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.664 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.639 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.635 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.636 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.636 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.636 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.636 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.634 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.635 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.636 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.636 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.635 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.623 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.624 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.619 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.620 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.618 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.618 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.618 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.620 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.617 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.618 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.294) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.683 (+/-0.296) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.683 (+/-0.296) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.683 (+/-0.296) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.699 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.699 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.682 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.698 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.682 (+/-0.296) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.682 (+/-0.296) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.682 (+/-0.296) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.665 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.665 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.665 (+/-0.317) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.665 (+/-0.317) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.665 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.662 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.636 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.637 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.636 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.637 (+/-0.324) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.636 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.610 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6990374309506142
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.680 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.680 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.685 (+/-0.297) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.665 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.665 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.665 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.665 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.601 (+/-0.159) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.632 (+/-0.279) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.636 (+/-0.280) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.636 (+/-0.280) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.643 (+/-0.285) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.647 (+/-0.285) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.625 (+/-0.184) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.629 (+/-0.185) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.625 (+/-0.184) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.619 (+/-0.181) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.623 (+/-0.183) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.658 (+/-0.302) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.655 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.646 (+/-0.300) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.646 (+/-0.301) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.646 (+/-0.301) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.628 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.627 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.626 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.626 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.625 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.626 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.629 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.627 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.629 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.623 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.624 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.619 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.620 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.618 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.618 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.618 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.620 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.617 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.618 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.295) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.696 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.694 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.678 (+/-0.294) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.677 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.676 (+/-0.296) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.659 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.659 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.659 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.660 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.660 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.660 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.660 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.660 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.660 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.660 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.660 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.660 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.661 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.636 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.637 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.636 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.637 (+/-0.324) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.636 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.610 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([ 10. , 15.84893192, 25.11886432, 39.81071706,
63.09573445, 100. , 158.48931925, 251.18864315,
398.10717055, 630.95734448, 1000. ]), 'kernel': ['rbf'], 'gamma': array([0.00144544, 0.00147231, 0.00149968, 0.00152757, 0.00155597,
0.00158489, 0.00161436, 0.00164437, 0.00167494, 0.00170608,
0.0017378 ])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}, 0.6990374309506142)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [2.84217094e-14 1.95156391e-18]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 0.0015848931924611123, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 100.0, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9355067328136074
测试集中，预测为舞弊样本的有： (array([ 370, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 8
训练模型SVC对测试样本的预测准确率： 0.9567682494684621
以上是第34次特征筛选。
第34次特征筛选，AUC值是： 0.8177805340726688
X_train_iter_svc.shape is: (1257, 18)
X_test_iter_svc.shape is: (1257, 18)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.690 (+/-0.299) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985566617198451
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6980753771951521
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6985566617198451
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6980753771951521
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.715 (+/-0.352) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.319) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.299) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.715 (+/-0.352) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.299) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.310) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.690 (+/-0.299) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.638 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.225) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.715 (+/-0.352) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.423) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.710 (+/-0.351) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.672 (+/-0.295) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.777 (+/-0.463) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.650 (+/-0.361) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.632 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.629 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.755 (+/-0.427) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.723 (+/-0.471) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.627 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.294) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.699 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.699 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.699 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.324) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.282) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.646 (+/-0.247) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6993584590650507
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.197) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.671 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.379) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.680 (+/-0.295) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.285) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.619 (+/-0.322) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.645 (+/-0.286) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.645 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.302) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.645 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.657 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.667 (+/-0.292) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.184) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.647 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.357) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.305) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.547 (+/-0.141) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.602 (+/-0.280) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.629 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.706 (+/-0.418) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.294) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.626 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.288) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.314) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.695 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.659 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.695 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.324) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.254) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.291) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.674 (+/-0.325) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.647 (+/-0.213) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.291) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.423) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.710 (+/-0.351) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.672 (+/-0.295) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.723 (+/-0.359) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.389) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.424) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.675 (+/-0.358) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.323) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.362) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.633 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.726 (+/-0.467) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.650 (+/-0.361) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.632 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.629 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.749 (+/-0.498) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.657 (+/-0.359) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.749 (+/-0.499) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.291) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.765 (+/-0.421) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.499) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.647 (+/-0.297) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.627 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.760 (+/-0.423) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.723 (+/-0.471) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.649 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.755 (+/-0.427) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.723 (+/-0.471) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.627 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.294) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.324) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.324) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.241) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.240) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.240) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.662 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.241) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.655 (+/-0.274) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.260) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.649 (+/-0.254) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.646 (+/-0.249) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.646 (+/-0.247) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6995187154753071
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.647 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.357) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.669 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.592 (+/-0.176) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.652 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.694 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.572 (+/-0.144) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.641 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.625 (+/-0.292) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.603 (+/-0.282) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.323) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.576 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.599 (+/-0.280) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.697 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.547 (+/-0.141) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.602 (+/-0.280) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.629 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.565 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.282) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.748 (+/-0.447) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.561 (+/-0.292) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.288) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.710 (+/-0.415) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.559 (+/-0.293) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.625 (+/-0.296) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.706 (+/-0.418) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.626 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.697 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.706 (+/-0.418) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.626 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.288) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.575 (+/-0.229) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.324) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.324) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.674 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.241) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.695 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.292) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.240) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.688 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.240) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.600 (+/-0.245) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.254) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.291) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.241) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.700 (+/-0.260) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.675 (+/-0.325) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.289) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.674 (+/-0.325) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.665 (+/-0.221) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.674 (+/-0.325) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.215) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.290) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.600 (+/-0.245) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.674 (+/-0.325) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.647 (+/-0.213) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.291) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7
循环迭代之前，delta is: [5.58793545e-09 0.00000000e+00]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 1e-08, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 9999999.999999994, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.8936924167257264
测试集中，预测为舞弊样本的有： (array([1248], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 1
训练模型SVC对测试样本的预测准确率： 0.8936924167257264
以上是第35次特征筛选。
第35次特征筛选，AUC值是： 0.5454545454545454
X_train_iter_svc.shape is: (1257, 17)
X_test_iter_svc.shape is: (1257, 17)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.715 (+/-0.352) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6987169181301014
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6980753771951521
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6987169181301014
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6980753771951521
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.715 (+/-0.352) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.310) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.715 (+/-0.352) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.310) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.620 (+/-0.321) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.715 (+/-0.352) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.299) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.715 (+/-0.352) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.299) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.643 (+/-0.320) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.690 (+/-0.300) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.423) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.685 (+/-0.297) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.677 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.646 (+/-0.364) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.634 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.435) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.626 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.287) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.330) for {'C': 100.0, 'kernel': 'linear'}
0.633 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.699 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.317) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.699 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.699 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.279) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.317) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6993584590650507
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.627 (+/-0.227) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.671 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.317) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.680 (+/-0.295) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.290) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.610 (+/-0.312) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.286) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.655 (+/-0.305) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.620 (+/-0.321) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.651 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.618 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.651 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.667 (+/-0.292) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.186) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.647 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.289) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.543 (+/-0.142) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.603 (+/-0.280) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.283) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.314) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.305) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.695 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.695 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.283) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.288) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.423) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.685 (+/-0.297) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.677 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.627 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.389) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.687 (+/-0.351) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.672 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.350) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.740 (+/-0.392) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.287) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.760 (+/-0.427) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.652 (+/-0.365) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.646 (+/-0.364) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.634 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.787 (+/-0.446) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.652 (+/-0.363) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.631 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.703 (+/-0.429) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.650 (+/-0.364) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.631 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.712 (+/-0.472) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.652 (+/-0.363) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.434) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.627 (+/-0.291) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.435) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.626 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.287) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.330) for {'C': 100.0, 'kernel': 'linear'}
0.633 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.320) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.320) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.319) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.239) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.297) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.682 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.297) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.329) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.663 (+/-0.305) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.330) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.659 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.298) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.330) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.657 (+/-0.283) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.298) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.330) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.279) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.328) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.317) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6995187154753071
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.647 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.289) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.669 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.369) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.652 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.684 (+/-0.362) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.598 (+/-0.284) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.641 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.610 (+/-0.220) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.599 (+/-0.284) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.564 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.599 (+/-0.280) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.697 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.543 (+/-0.142) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.603 (+/-0.280) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.538 (+/-0.143) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.283) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.534 (+/-0.144) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.283) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.320) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.283) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.283) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.697 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.283) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.575 (+/-0.229) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.320) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.320) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.694 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.690 (+/-0.287) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.319) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.239) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.600 (+/-0.245) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.284) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.287) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.669 (+/-0.247) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.287) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.327) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.671 (+/-0.269) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.288) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.326) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.251) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.288) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.326) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.246) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.288) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.326) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.600 (+/-0.245) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.288) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7
循环迭代之前，delta is: [5.58793545e-09 0.00000000e+00]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 1e-08, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 9999999.999999994, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.8936924167257264
测试集中，预测为舞弊样本的有： (array([1248], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 1
训练模型SVC对测试样本的预测准确率： 0.8936924167257264
以上是第36次特征筛选。
第36次特征筛选，AUC值是： 0.5454545454545454
X_train_iter_svc.shape is: (1257, 16)
X_test_iter_svc.shape is: (1257, 16)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.665 (+/-0.225) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.628 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6979151207848958
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6983958900156649
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6979151207848958
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.690 (+/-0.300) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.310) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.300) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.310) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.620 (+/-0.321) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.225) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.299) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.299) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.635 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.225) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.690 (+/-0.299) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.643 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.723 (+/-0.359) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.669 (+/-0.295) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.781 (+/-0.418) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.655 (+/-0.362) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.634 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.434) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.613 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.287) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.317) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.657 (+/-0.283) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6991982026547944
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.624 (+/-0.227) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.671 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.317) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.671 (+/-0.291) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.633 (+/-0.197) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.610 (+/-0.312) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.671 (+/-0.291) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.622 (+/-0.186) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.655 (+/-0.305) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.620 (+/-0.321) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.671 (+/-0.291) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.186) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.671 (+/-0.291) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.186) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.655 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.186) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.647 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.613 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.543 (+/-0.142) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.283) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.595 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.625 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.628 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.314) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.695 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.695 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.283) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.317) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.723 (+/-0.359) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.669 (+/-0.295) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.389) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.715 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.669 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.636 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.710 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.632 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.740 (+/-0.392) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.675 (+/-0.360) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.637 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.632 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.760 (+/-0.427) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.657 (+/-0.360) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.635 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.631 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.781 (+/-0.418) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.655 (+/-0.362) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.634 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.787 (+/-0.446) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.359) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.705 (+/-0.426) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.643 (+/-0.372) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.713 (+/-0.471) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.651 (+/-0.371) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.434) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.308) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.434) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.613 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.287) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.318) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.682 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.288) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.327) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.320) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.664 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.663 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.320) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.661 (+/-0.297) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.663 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.287) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.657 (+/-0.283) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.329) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6995187154753071
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.647 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.613 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.669 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.646 (+/-0.367) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.652 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.684 (+/-0.362) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.283) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.650 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.608 (+/-0.220) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.280) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.564 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.612 (+/-0.281) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.697 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.543 (+/-0.142) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.283) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.538 (+/-0.143) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.282) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.533 (+/-0.145) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.288) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.608 (+/-0.291) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.591 (+/-0.295) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.697 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.595 (+/-0.298) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.625 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.628 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.575 (+/-0.229) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.691 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.694 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.294) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.318) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.294) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.600 (+/-0.245) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.284) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.670 (+/-0.247) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.283) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.326) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.320) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.646 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.661 (+/-0.305) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.320) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.251) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.661 (+/-0.304) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.246) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.600 (+/-0.245) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.317) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7
循环迭代之前，delta is: [5.58793545e-09 0.00000000e+00]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 1e-08, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 9999999.999999994, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.8936924167257264
测试集中，预测为舞弊样本的有： (array([1248], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 1
训练模型SVC对测试样本的预测准确率： 0.8936924167257264
以上是第37次特征筛选。
第37次特征筛选，AUC值是： 0.5454545454545454
X_train_iter_svc.shape is: (1257, 15)
X_test_iter_svc.shape is: (1257, 15)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.665 (+/-0.225) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.647 (+/-0.212) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.616 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6979151207848958
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6983958900156649
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6979151207848958
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.690 (+/-0.300) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.310) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.677 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.225) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.290) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.601 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.655 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.619 (+/-0.300) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.225) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.655 (+/-0.286) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.300) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.286) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.604 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.715 (+/-0.416) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.362) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.289) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.700 (+/-0.421) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.372) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.608 (+/-0.291) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.357) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.617 (+/-0.301) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.676 (+/-0.243) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.654 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.323) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6991982026547944
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.652 (+/-0.313) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.671 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.380) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.671 (+/-0.291) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.188) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.301) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.671 (+/-0.291) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.624 (+/-0.188) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.634 (+/-0.290) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.671 (+/-0.291) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.624 (+/-0.188) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.280) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.605 (+/-0.311) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.671 (+/-0.291) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.184) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.627 (+/-0.280) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.188) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.280) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.697 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.225) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.641 (+/-0.286) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.281) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.604 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.535 (+/-0.144) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.276) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.603 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.551 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.606 (+/-0.287) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.304) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.647 (+/-0.212) for {'C': 1.0, 'kernel': 'linear'}
0.618 (+/-0.276) for {'C': 10.0, 'kernel': 'linear'}
0.612 (+/-0.304) for {'C': 100.0, 'kernel': 'linear'}
0.616 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.315) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.694 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.694 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.600 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.302) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.694 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.208) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.575 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.317) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.323) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.695 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.715 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.651 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.740 (+/-0.392) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.350) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.648 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.710 (+/-0.352) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.672 (+/-0.355) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.298) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.716 (+/-0.414) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.657 (+/-0.359) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.613 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.740 (+/-0.449) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.362) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.688 (+/-0.374) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.650 (+/-0.361) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.701 (+/-0.420) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.647 (+/-0.363) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.609 (+/-0.290) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.701 (+/-0.420) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.373) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.609 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.725 (+/-0.457) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.637 (+/-0.373) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.608 (+/-0.291) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.700 (+/-0.421) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.372) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.608 (+/-0.291) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.357) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.617 (+/-0.301) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.321) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.320) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.276) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.663 (+/-0.233) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.679 (+/-0.291) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.239) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.244) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.652 (+/-0.245) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.323) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6995187154753071
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.697 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.225) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.641 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.281) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.635 (+/-0.201) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.641 (+/-0.365) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.631 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.618 (+/-0.188) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.639 (+/-0.366) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.587 (+/-0.163) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.277) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.553 (+/-0.147) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.276) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.606 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.697 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.535 (+/-0.144) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.276) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.603 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.530 (+/-0.146) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.279) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.603 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.528 (+/-0.147) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.279) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.603 (+/-0.293) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.551 (+/-0.297) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.602 (+/-0.287) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.293) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.550 (+/-0.298) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.605 (+/-0.288) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.293) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.697 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.551 (+/-0.297) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.606 (+/-0.287) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.304) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.647 (+/-0.212) for {'C': 1.0, 'kernel': 'linear'}
0.618 (+/-0.276) for {'C': 10.0, 'kernel': 'linear'}
0.612 (+/-0.304) for {'C': 100.0, 'kernel': 'linear'}
0.616 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.600 (+/-0.245) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.694 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.697 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.717 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.696 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.700 (+/-0.347) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.320) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.692 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.682 (+/-0.370) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.321) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.677 (+/-0.265) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.370) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.600 (+/-0.245) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.208) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.621 (+/-0.143) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.304) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.589 (+/-0.141) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.679 (+/-0.292) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.567 (+/-0.124) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.555 (+/-0.126) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.600 (+/-0.245) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.575 (+/-0.233) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.317) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.323) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.695 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.05 0.17 0.08 6
1 0.99 0.97 0.98 623
avg / total 0.98 0.96 0.97 629
本轮grid search结果，得到最好的参数选择是： {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7171453747217412
循环迭代之前，delta is: [8.41510681e+06 9.99000000e-06]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1000000. , 1096478.19614319, 1202264.43461741,
1318256.73855641, 1445439.77074593, 1584893.19246111,
1737800.82874938, 1905460.71796325, 2089296.13085404,
2290867.65276777, 2511886.43150958]), 'kernel': ['rbf'], 'gamma': array([1.00000000e-06, 1.58489319e-06, 2.51188643e-06, 3.98107171e-06,
6.30957344e-06, 1.00000000e-05, 1.58489319e-05, 2.51188643e-05,
3.98107171e-05, 6.30957344e-05, 1.00000000e-04])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.659 (+/-0.287) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.649 (+/-0.288) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.643 (+/-0.285) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.655 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.646 (+/-0.284) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.641 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.620 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.618 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.660 (+/-0.219) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.287) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.630 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.357) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.643 (+/-0.285) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.643 (+/-0.279) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.649 (+/-0.286) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.628 (+/-0.299) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.620 (+/-0.299) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.680 (+/-0.294) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.287) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.286) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.634 (+/-0.289) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.277) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.286) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.649 (+/-0.286) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.628 (+/-0.299) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.619 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.671 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.287) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.286) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.634 (+/-0.289) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.633 (+/-0.277) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.286) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.651 (+/-0.286) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.625 (+/-0.298) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.632 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.619 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.667 (+/-0.292) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.288) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.287) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.641 (+/-0.288) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.633 (+/-0.277) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.646 (+/-0.284) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.644 (+/-0.285) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.623 (+/-0.298) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.624 (+/-0.299) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.619 (+/-0.300) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.653 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.648 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.641 (+/-0.288) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.641 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.639 (+/-0.284) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.623 (+/-0.298) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.627 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.619 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.616 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.684 (+/-0.350) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.653 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.648 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.643 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.643 (+/-0.285) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.655 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.639 (+/-0.284) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.623 (+/-0.298) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.623 (+/-0.299) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.619 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.616 (+/-0.302) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.684 (+/-0.350) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.653 (+/-0.287) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.636 (+/-0.287) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.632 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.639 (+/-0.285) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.286) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.637 (+/-0.284) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.623 (+/-0.298) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.624 (+/-0.299) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.618 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.616 (+/-0.302) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.684 (+/-0.350) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.653 (+/-0.287) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.632 (+/-0.289) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.632 (+/-0.278) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.639 (+/-0.285) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.655 (+/-0.287) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.284) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.623 (+/-0.298) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.624 (+/-0.299) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.617 (+/-0.302) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.302) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.680 (+/-0.350) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.651 (+/-0.287) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.632 (+/-0.289) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.632 (+/-0.278) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.642 (+/-0.287) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.292) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.629 (+/-0.298) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.621 (+/-0.299) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.623 (+/-0.300) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.617 (+/-0.302) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.680 (+/-0.350) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.287) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.635 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.632 (+/-0.278) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.645 (+/-0.286) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.648 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.625 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.620 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.623 (+/-0.300) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.617 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.357) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.617 (+/-0.301) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.680 (+/-0.290) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.662 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.311) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.313) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.311) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.313) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.305) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.312) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.314) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.293) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.312) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.314) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.315) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.292) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.313) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.315) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.293) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.303) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.305) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.293) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.313) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.315) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.303) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.307) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.313) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.312) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.681 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.641 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.290) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.611 (+/-0.279) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.609 (+/-0.276) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.278) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.281) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.630 (+/-0.289) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.628 (+/-0.290) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.627 (+/-0.287) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.618 (+/-0.290) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.153) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.609 (+/-0.276) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.278) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.621 (+/-0.280) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.292) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.616 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.645 (+/-0.362) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.291) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.613 (+/-0.279) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.611 (+/-0.276) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.628 (+/-0.291) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.633 (+/-0.292) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.616 (+/-0.301) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.644 (+/-0.363) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.610 (+/-0.276) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.615 (+/-0.278) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.620 (+/-0.280) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.632 (+/-0.288) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.292) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.616 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.614 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.611 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.642 (+/-0.364) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.613 (+/-0.292) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.610 (+/-0.276) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.278) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.620 (+/-0.280) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.631 (+/-0.289) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.624 (+/-0.290) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.615 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.302) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.611 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.303) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.641 (+/-0.365) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.614 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.611 (+/-0.278) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.621 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.626 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.631 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.624 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.615 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.611 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.641 (+/-0.365) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.606 (+/-0.280) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.609 (+/-0.279) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.623 (+/-0.278) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.291) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.291) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.612 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.611 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.611 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.303) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.640 (+/-0.366) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.606 (+/-0.280) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.609 (+/-0.279) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.620 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.288) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.291) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.290) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.612 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.638 (+/-0.367) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.604 (+/-0.281) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.614 (+/-0.278) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.620 (+/-0.278) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.632 (+/-0.288) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.291) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.626 (+/-0.292) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.612 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.612 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.303) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.638 (+/-0.367) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.604 (+/-0.281) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.614 (+/-0.278) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.280) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.634 (+/-0.291) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.291) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.611 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.612 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.612 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.614 (+/-0.303) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.639 (+/-0.366) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.606 (+/-0.280) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.618 (+/-0.278) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.280) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.633 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.611 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.612 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.614 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.647 (+/-0.212) for {'C': 1.0, 'kernel': 'linear'}
0.618 (+/-0.276) for {'C': 10.0, 'kernel': 'linear'}
0.612 (+/-0.304) for {'C': 100.0, 'kernel': 'linear'}
0.616 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.697 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.298) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.352) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.700 (+/-0.346) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.367) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.696 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.299) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.351) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.683 (+/-0.367) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.367) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.314) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.298) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.353) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.683 (+/-0.367) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.367) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.314) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.298) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.718 (+/-0.353) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.352) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.683 (+/-0.368) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.366) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.699 (+/-0.345) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.368) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.367) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.699 (+/-0.346) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.368) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.367) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.355) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.698 (+/-0.345) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.368) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.299) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.355) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.698 (+/-0.347) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.368) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.314) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.299) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.354) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.698 (+/-0.346) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.314) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.314) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.300) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.355) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.313) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.314) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.717 (+/-0.352) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.700 (+/-0.347) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.683 (+/-0.367) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.695 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.06 0.17 0.09 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7182671695935362
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1584893.19246111, 1614358.55682649, 1644371.72321493,
1674942.87602644, 1706082.38900313, 1737800.82874938,
1770108.95831742, 1803017.74085957, 1836538.34334835,
1870682.1403658 , 1905460.71796325]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-06, 4.36515832e-06, 4.78630092e-06, 5.24807460e-06,
5.75439937e-06, 6.30957344e-06, 6.91830971e-06, 7.58577575e-06,
8.31763771e-06, 9.12010839e-06, 1.00000000e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.7182671695935362)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [1.52907636e+05 3.69042656e-06]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1584893.19246111, 1614358.55682649, 1644371.72321493,
1674942.87602644, 1706082.38900313, 1737800.82874938,
1770108.95831742, 1803017.74085957, 1836538.34334835,
1870682.1403658 , 1905460.71796325]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-06, 4.36515832e-06, 4.78630092e-06, 5.24807460e-06,
5.75439937e-06, 6.30957344e-06, 6.91830971e-06, 7.58577575e-06,
8.31763771e-06, 9.12010839e-06, 1.00000000e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.641 (+/-0.288) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.639 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.658 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.641 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.276) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.651 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.639 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.288) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.641 (+/-0.286) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.683 (+/-0.354) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.641 (+/-0.286) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.276) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.652 (+/-0.292) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.638 (+/-0.287) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.279) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.288) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.683 (+/-0.354) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.276) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.643 (+/-0.285) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.638 (+/-0.287) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.638 (+/-0.288) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.288) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.666 (+/-0.305) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.637 (+/-0.277) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.642 (+/-0.286) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.638 (+/-0.287) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.643 (+/-0.287) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.666 (+/-0.305) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.635 (+/-0.277) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.642 (+/-0.286) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.638 (+/-0.287) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.288) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.643 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.645 (+/-0.286) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.669 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.635 (+/-0.277) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.644 (+/-0.285) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.634 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.655 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.643 (+/-0.287) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.355) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.277) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.640 (+/-0.286) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.634 (+/-0.287) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.635 (+/-0.279) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.355) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.277) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.642 (+/-0.286) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.635 (+/-0.286) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.635 (+/-0.279) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.278) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.646 (+/-0.284) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.645 (+/-0.286) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.355) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.277) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.642 (+/-0.286) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.635 (+/-0.286) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.635 (+/-0.279) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.278) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.644 (+/-0.285) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.637 (+/-0.278) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.355) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.639 (+/-0.285) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.645 (+/-0.287) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.647 (+/-0.284) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.634 (+/-0.287) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.644 (+/-0.285) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.637 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.355) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.639 (+/-0.285) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.645 (+/-0.287) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.647 (+/-0.284) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.634 (+/-0.287) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.357) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.617 (+/-0.301) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.696 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.697 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.307) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.696 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.697 (+/-0.303) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.307) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.303) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.696 (+/-0.301) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.307) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.302) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.307) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.302) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.696 (+/-0.301) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.307) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.696 (+/-0.301) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.305) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.308) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.303) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.302) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.305) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.305) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.308) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.303) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.303) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.303) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.305) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.308) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.303) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.303) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.303) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.308) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.696 (+/-0.305) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.303) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.697 (+/-0.305) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.305) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.308) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.696 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.697 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.621 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.621 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.627 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.626 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.620 (+/-0.281) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.623 (+/-0.283) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.631 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.621 (+/-0.279) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.624 (+/-0.278) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.625 (+/-0.280) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.627 (+/-0.291) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.620 (+/-0.281) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.629 (+/-0.291) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.623 (+/-0.283) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.631 (+/-0.289) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.621 (+/-0.279) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.627 (+/-0.279) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.627 (+/-0.291) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.620 (+/-0.281) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.628 (+/-0.293) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.631 (+/-0.293) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.631 (+/-0.289) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.621 (+/-0.279) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.627 (+/-0.279) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.627 (+/-0.291) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.282) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.294) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.293) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.631 (+/-0.289) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.278) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.626 (+/-0.280) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.282) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.628 (+/-0.293) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.294) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.631 (+/-0.289) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.278) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.628 (+/-0.281) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.283) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.628 (+/-0.293) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.294) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.278) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.635 (+/-0.290) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.283) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.625 (+/-0.294) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.294) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.278) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.634 (+/-0.290) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.283) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.294) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.294) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.278) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.628 (+/-0.277) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.627 (+/-0.280) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.633 (+/-0.291) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.626 (+/-0.292) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.283) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.294) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.629 (+/-0.294) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.620 (+/-0.278) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.627 (+/-0.280) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.633 (+/-0.291) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.636 (+/-0.294) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.294) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.294) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.620 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.620 (+/-0.280) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.627 (+/-0.280) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.633 (+/-0.291) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.636 (+/-0.294) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.294) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.629 (+/-0.294) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.647 (+/-0.212) for {'C': 1.0, 'kernel': 'linear'}
0.618 (+/-0.276) for {'C': 10.0, 'kernel': 'linear'}
0.612 (+/-0.304) for {'C': 100.0, 'kernel': 'linear'}
0.616 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.695 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.694 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.694 (+/-0.305) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.353) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.307) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.694 (+/-0.305) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.354) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.307) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.694 (+/-0.305) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.354) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.307) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.352) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.354) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.307) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.352) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.352) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.303) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.351) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.304) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.693 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.304) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.352) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.307) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.352) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.307) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.691 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.695 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.06 0.17 0.09 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7184274260037926
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1770108.95831742, 1776642.30820362, 1783199.77223292,
1789781.43940896, 1796387.39906389, 1803017.74085957,
1809672.55478879, 1816351.93117652, 1823055.96068107,
1829784.73429542, 1836538.34334835]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-06, 5.86138165e-06, 5.97035287e-06, 6.08135001e-06,
6.19441075e-06, 6.30957344e-06, 6.42687717e-06, 6.54636174e-06,
6.66806769e-06, 6.79203633e-06, 6.91830971e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.7184274260037926)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [65216.91211019 0. ]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 6.309573444801928e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 1803017.7408595693, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9532246633593197
测试集中，预测为舞弊样本的有： (array([ 0, 1, 2, ..., 1254, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 1126
训练模型SVC对测试样本的预测准确率： 0.19844082211197733
以上是第38次特征筛选。
第38次特征筛选，AUC值是： 0.5067123887348606
X_train_iter_svc.shape is: (1257, 14)
X_test_iter_svc.shape is: (1257, 14)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6979146054909721
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.589 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.697594607964383
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6979146054909721
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.697594607964383
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.665 (+/-0.225) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.309) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.660 (+/-0.290) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.384) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.167) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.601 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.646 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.595 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.646 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.165) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.596 (+/-0.193) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.576 (+/-0.207) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.731 (+/-0.365) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.606 (+/-0.165) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.597 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.591 (+/-0.194) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.581 (+/-0.201) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.707 (+/-0.415) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.620 (+/-0.289) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.586 (+/-0.170) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.201) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.671 (+/-0.375) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.289) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.587 (+/-0.170) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.195) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.235) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.221) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.301) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.654 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6991976873608706
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.652 (+/-0.313) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.642 (+/-0.204) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.310) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.204) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.185) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.598 (+/-0.298) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.642 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.182) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.625 (+/-0.282) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.384) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.182) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.597 (+/-0.161) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.605 (+/-0.311) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.182) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.597 (+/-0.161) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.196) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.618 (+/-0.182) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.594 (+/-0.157) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.581 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.697 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.225) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.604 (+/-0.176) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.594 (+/-0.162) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.589 (+/-0.194) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.581 (+/-0.201) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.831 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.535 (+/-0.144) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.277) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.582 (+/-0.166) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.201) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.551 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.618 (+/-0.278) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.578 (+/-0.172) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.315) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.694 (+/-0.305) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.657 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.694 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.600 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.207) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.705 (+/-0.338) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.575 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.304) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.362) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.04 0.17 0.07 6
1 0.99 0.96 0.98 623
avg / total 0.98 0.96 0.97 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7051261439525104
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.634 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.611 (+/-0.184) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.185) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.606 (+/-0.165) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.179) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.616 (+/-0.182) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.602 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.602 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.597 (+/-0.195) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.655 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.597 (+/-0.161) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.619 (+/-0.170) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.610 (+/-0.168) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.614 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.598 (+/-0.191) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.598 (+/-0.192) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.592 (+/-0.192) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.592 (+/-0.194) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.653 (+/-0.286) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.636 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.160) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.601 (+/-0.157) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.172) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.622 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.599 (+/-0.192) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.192) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.193) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.592 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.645 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.625 (+/-0.289) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.160) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.597 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.618 (+/-0.172) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.610 (+/-0.180) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.188) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.193) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.591 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.623 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.621 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.608 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.601 (+/-0.159) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.173) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.170) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.589 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.595 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.592 (+/-0.193) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.590 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.620 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.614 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.184) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.586 (+/-0.170) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.193) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.594 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.597 (+/-0.175) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.616 (+/-0.183) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.184) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.177) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.170) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.193) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.593 (+/-0.193) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.195) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.196) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.617 (+/-0.290) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.594 (+/-0.175) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.622 (+/-0.212) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.184) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.177) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.170) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.171) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.594 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.617 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.593 (+/-0.175) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.630 (+/-0.224) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.184) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.174) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.170) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.172) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.593 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.289) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.593 (+/-0.175) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.215) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.185) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.175) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.170) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.172) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.591 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.623 (+/-0.289) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.599 (+/-0.176) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.215) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.185) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.175) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.170) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.172) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.591 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.589 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.195) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.301) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.680 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.662 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.289) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.661 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.308) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.308) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.604 (+/-0.176) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.614 (+/-0.292) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.583 (+/-0.151) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.580 (+/-0.144) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.163) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.162) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.603 (+/-0.183) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.603 (+/-0.182) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.194) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.616 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.586 (+/-0.177) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.580 (+/-0.144) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.153) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.165) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.165) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.599 (+/-0.279) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.586 (+/-0.176) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.583 (+/-0.145) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.590 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.602 (+/-0.164) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.613 (+/-0.184) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.191) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.196) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.194) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.607 (+/-0.280) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.579 (+/-0.151) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.591 (+/-0.157) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.173) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.605 (+/-0.177) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.587 (+/-0.195) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.603 (+/-0.278) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.577 (+/-0.143) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.145) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.600 (+/-0.148) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.166) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.195) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.607 (+/-0.277) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.146) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.593 (+/-0.152) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.185) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.166) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.586 (+/-0.196) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.195) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.604 (+/-0.277) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.581 (+/-0.144) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.154) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.163) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.167) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.196) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.195) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.197) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.606 (+/-0.277) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.578 (+/-0.143) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.596 (+/-0.158) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.163) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.578 (+/-0.172) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.173) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.196) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.197) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.610 (+/-0.278) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.576 (+/-0.143) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.603 (+/-0.180) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.179) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.163) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.578 (+/-0.172) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.173) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.587 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.196) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.197) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.611 (+/-0.277) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.576 (+/-0.143) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.597 (+/-0.166) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.179) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.585 (+/-0.164) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.578 (+/-0.172) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.173) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.587 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.197) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.278) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.583 (+/-0.146) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.597 (+/-0.166) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.179) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.585 (+/-0.164) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.578 (+/-0.172) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.173) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.587 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.197) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.696 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.716 (+/-0.351) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.699 (+/-0.346) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.681 (+/-0.365) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.298) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.698 (+/-0.345) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.366) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.717 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.741 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.681 (+/-0.365) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.715 (+/-0.352) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.742 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.365) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.336) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.345) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.338) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.306) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.340) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.346) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.337) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.655 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.339) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.704 (+/-0.347) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.362) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.707 (+/-0.339) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.703 (+/-0.347) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.362) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.707 (+/-0.339) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.703 (+/-0.346) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.362) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.308) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.304) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.300) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.707 (+/-0.339) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.704 (+/-0.344) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.362) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.655 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.07 0.17 0.10 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7423051158380741
循环迭代之前，delta is: [3.69042656e+06 6.01892829e-06]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([3981071.70553497, 4365158.32240166, 4786300.92322638,
5248074.60249772, 5754399.37337156, 6309573.44480193,
6918309.70918936, 7585775.75029184, 8317637.7110267 ,
9120108.39355909, 9999999.99999999]), 'kernel': ['rbf'], 'gamma': array([2.51188643e-06, 2.75422870e-06, 3.01995172e-06, 3.31131121e-06,
3.63078055e-06, 3.98107171e-06, 4.36515832e-06, 4.78630092e-06,
5.24807460e-06, 5.75439937e-06, 6.30957344e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.595 (+/-0.160) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.627 (+/-0.200) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.597 (+/-0.161) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.287) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.600 (+/-0.164) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.159) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.623 (+/-0.184) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.607 (+/-0.180) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.622 (+/-0.174) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.618 (+/-0.172) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.154) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.652 (+/-0.295) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.597 (+/-0.161) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.641 (+/-0.289) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.608 (+/-0.184) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.159) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.623 (+/-0.184) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.596 (+/-0.192) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.630 (+/-0.189) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.608 (+/-0.161) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.154) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.294) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.597 (+/-0.161) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.639 (+/-0.290) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.604 (+/-0.180) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.155) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.159) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.623 (+/-0.184) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.596 (+/-0.192) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.639 (+/-0.202) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.608 (+/-0.161) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.177) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.294) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.598 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.287) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.158) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.602 (+/-0.166) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.616 (+/-0.182) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.639 (+/-0.202) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.605 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.603 (+/-0.181) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.294) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.598 (+/-0.160) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.636 (+/-0.286) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.181) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.600 (+/-0.158) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.605 (+/-0.179) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.614 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.639 (+/-0.202) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.595 (+/-0.176) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.294) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.592 (+/-0.155) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.642 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.159) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.614 (+/-0.182) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.594 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.639 (+/-0.202) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.591 (+/-0.173) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.604 (+/-0.180) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.294) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.596 (+/-0.155) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.652 (+/-0.289) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.152) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.605 (+/-0.180) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.605 (+/-0.163) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.594 (+/-0.191) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.627 (+/-0.197) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.176) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.602 (+/-0.178) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.644 (+/-0.288) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.596 (+/-0.155) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.649 (+/-0.288) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.588 (+/-0.150) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.191) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.608 (+/-0.164) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.596 (+/-0.191) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.626 (+/-0.188) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.592 (+/-0.177) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.609 (+/-0.184) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.594 (+/-0.155) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.657 (+/-0.303) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.613 (+/-0.186) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.588 (+/-0.150) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.191) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.595 (+/-0.176) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.191) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.624 (+/-0.189) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.592 (+/-0.177) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.609 (+/-0.184) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.594 (+/-0.155) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.657 (+/-0.303) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.613 (+/-0.186) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.596 (+/-0.174) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.584 (+/-0.170) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.598 (+/-0.183) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.593 (+/-0.190) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.611 (+/-0.204) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.614 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.597 (+/-0.158) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.674 (+/-0.354) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.612 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.586 (+/-0.184) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.584 (+/-0.170) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.595 (+/-0.176) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.593 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.607 (+/-0.201) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.195) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.679 (+/-0.287) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.696 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.307) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.294) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.294) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.680 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.308) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.696 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.289) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.680 (+/-0.293) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.308) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.663 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.680 (+/-0.294) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.302) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.306) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.679 (+/-0.290) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.289) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.679 (+/-0.294) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.296) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.663 (+/-0.314) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.302) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.307) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.678 (+/-0.289) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.309) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.679 (+/-0.294) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.311) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.296) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.304) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.308) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.678 (+/-0.289) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.309) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.314) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.663 (+/-0.311) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.295) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.305) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.308) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.678 (+/-0.289) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.309) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.315) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.310) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.316) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.662 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.590 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.610 (+/-0.170) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.589 (+/-0.154) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.625 (+/-0.278) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.588 (+/-0.160) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.157) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.161) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.608 (+/-0.186) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.602 (+/-0.180) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.601 (+/-0.165) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.601 (+/-0.173) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.586 (+/-0.149) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.610 (+/-0.170) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.589 (+/-0.154) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.628 (+/-0.280) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.596 (+/-0.183) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.160) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.608 (+/-0.186) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.608 (+/-0.185) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.594 (+/-0.162) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.586 (+/-0.149) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.164) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.589 (+/-0.154) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.627 (+/-0.281) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.595 (+/-0.183) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.155) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.160) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.607 (+/-0.187) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.611 (+/-0.190) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.162) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.144) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.164) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.625 (+/-0.281) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.600 (+/-0.185) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.596 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.594 (+/-0.169) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.183) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.192) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.611 (+/-0.191) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.162) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.146) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.629 (+/-0.281) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.148) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.184) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.597 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.193) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.611 (+/-0.191) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.145) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.587 (+/-0.154) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.284) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.600 (+/-0.148) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.184) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.596 (+/-0.183) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.610 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.145) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.164) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.284) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.593 (+/-0.142) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.184) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.588 (+/-0.161) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.193) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.602 (+/-0.183) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.145) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.164) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.284) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.590 (+/-0.141) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.194) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.590 (+/-0.162) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.589 (+/-0.193) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.602 (+/-0.183) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.152) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.589 (+/-0.142) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.194) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.577 (+/-0.170) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.589 (+/-0.193) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.599 (+/-0.183) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.152) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.166) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.171) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.577 (+/-0.170) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.587 (+/-0.192) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.590 (+/-0.187) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.588 (+/-0.194) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.152) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.590 (+/-0.159) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.587 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.171) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.576 (+/-0.170) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.587 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.590 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.595 (+/-0.185) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.694 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.694 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.299) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.701 (+/-0.346) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.353) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.715 (+/-0.352) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.694 (+/-0.300) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.307) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.299) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.352) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.366) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.353) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.699 (+/-0.345) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.694 (+/-0.300) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.306) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.352) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.366) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.353) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.699 (+/-0.344) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.304) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.694 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.307) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.351) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.305) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.366) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.353) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.698 (+/-0.344) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.300) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.307) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.743 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.675 (+/-0.292) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.292) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.365) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.354) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.742 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.675 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.366) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.353) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.309) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.726 (+/-0.314) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.675 (+/-0.292) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.675 (+/-0.292) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.365) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.699 (+/-0.345) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.309) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.311) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.292) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.365) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.699 (+/-0.345) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.366) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.309) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.312) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.659 (+/-0.311) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.682 (+/-0.365) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.345) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.366) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.310) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.367) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.312) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.682 (+/-0.366) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.707 (+/-0.339) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.680 (+/-0.366) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.694 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.708 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.682 (+/-0.366) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.707 (+/-0.339) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.705 (+/-0.345) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.07 0.17 0.10 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7426256286585868
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([5248074.60249772, 5345643.5939697 , 5445026.5284242 ,
5546257.1295791 , 5649369.74812302, 5754399.37337156,
5861381.64514028, 5970352.86583836, 6081350.01278718,
6194410.75076781, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([3.63078055e-06, 3.69828180e-06, 3.76703799e-06, 3.83707245e-06,
3.90840896e-06, 3.98107171e-06, 4.05508535e-06, 4.13047502e-06,
4.20726628e-06, 4.28548520e-06, 4.36515832e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}, 0.7426256286585868)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [555174.07143037 0. ]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([5248074.60249772, 5345643.5939697 , 5445026.5284242 ,
5546257.1295791 , 5649369.74812302, 5754399.37337156,
5861381.64514028, 5970352.86583836, 6081350.01278718,
6194410.75076781, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([3.63078055e-06, 3.69828180e-06, 3.76703799e-06, 3.83707245e-06,
3.90840896e-06, 3.98107171e-06, 4.05508535e-06, 4.13047502e-06,
4.20726628e-06, 4.28548520e-06, 4.36515832e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.609 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.599 (+/-0.158) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.604 (+/-0.163) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.158) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.621 (+/-0.189) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.165) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.629 (+/-0.213) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.602 (+/-0.166) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.601 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.604 (+/-0.163) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.160) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.621 (+/-0.189) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.165) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.629 (+/-0.213) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.602 (+/-0.166) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.604 (+/-0.163) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.176) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.611 (+/-0.185) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.604 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.176) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.612 (+/-0.165) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.608 (+/-0.185) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.605 (+/-0.182) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.158) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.603 (+/-0.163) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.176) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.179) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.158) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.176) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.629 (+/-0.199) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.179) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.605 (+/-0.181) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.171) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.603 (+/-0.180) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.171) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.180) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.171) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.622 (+/-0.198) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.180) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.159) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.622 (+/-0.198) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.630 (+/-0.224) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.180) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.160) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.622 (+/-0.198) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.630 (+/-0.224) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.603 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.195) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.695 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.694 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.695 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.697 (+/-0.307) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.305) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.695 (+/-0.302) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.694 (+/-0.298) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.293) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.697 (+/-0.307) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.305) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.695 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.695 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.695 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.307) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.299) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.313) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.313) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.299) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.313) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.292) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.680 (+/-0.296) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.293) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.680 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.600 (+/-0.185) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.596 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.600 (+/-0.172) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.600 (+/-0.165) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.594 (+/-0.169) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.185) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.596 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.599 (+/-0.172) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.600 (+/-0.165) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.594 (+/-0.169) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.185) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.159) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.159) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.600 (+/-0.165) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.607 (+/-0.185) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.602 (+/-0.190) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.185) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.602 (+/-0.183) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.159) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.596 (+/-0.159) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.597 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.607 (+/-0.185) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.602 (+/-0.190) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.185) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.183) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.596 (+/-0.159) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.148) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.607 (+/-0.185) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.599 (+/-0.183) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.596 (+/-0.159) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.148) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.606 (+/-0.185) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.598 (+/-0.184) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.183) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.594 (+/-0.158) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.148) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.606 (+/-0.185) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.599 (+/-0.183) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.602 (+/-0.183) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.593 (+/-0.158) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.148) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.606 (+/-0.185) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.601 (+/-0.184) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.602 (+/-0.183) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.593 (+/-0.158) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.164) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.148) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.599 (+/-0.182) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.606 (+/-0.185) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.601 (+/-0.184) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.602 (+/-0.183) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.597 (+/-0.168) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.593 (+/-0.158) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.164) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.148) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.161) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.599 (+/-0.182) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.599 (+/-0.181) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.601 (+/-0.184) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.602 (+/-0.183) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.596 (+/-0.168) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.593 (+/-0.158) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.148) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.599 (+/-0.183) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.599 (+/-0.181) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.598 (+/-0.184) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.692 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.350) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.718 (+/-0.351) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.291) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.693 (+/-0.304) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.306) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.692 (+/-0.305) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.350) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.718 (+/-0.351) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.291) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.693 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.306) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.692 (+/-0.306) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.301) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.718 (+/-0.351) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.306) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.692 (+/-0.306) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.718 (+/-0.351) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.306) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.692 (+/-0.306) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.743 (+/-0.316) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.306) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.300) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.743 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.305) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.743 (+/-0.316) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.305) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.350) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.743 (+/-0.316) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.305) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.350) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.350) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.305) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.350) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.350) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.316) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.292) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.677 (+/-0.292) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.350) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.350) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.675 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.677 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.07 0.17 0.10 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7427858850688432
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([5546257.1295791 , 5566727.98150948, 5587274.38994019,
5607896.6337451 , 5628594.99282729, 5649369.74812302,
5670221.18160543, 5691149.5762884 , 5712155.21623043,
5733238.38653839, 5754399.37337156]), 'kernel': ['rbf'], 'gamma': array([3.90840896e-06, 3.92283463e-06, 3.93731354e-06, 3.95184589e-06,
3.96643188e-06, 3.98107171e-06, 3.99576557e-06, 4.01051366e-06,
4.02531619e-06, 4.04017335e-06, 4.05508535e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}, 0.7427858850688432)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [105029.62524854 0. ]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 3.981071705534978e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 5649369.748123019, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9440113394755493
测试集中，预测为舞弊样本的有： (array([ 0, 1, 2, ..., 1254, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 1055
训练模型SVC对测试样本的预测准确率： 0.24875974486180014
以上是第39次特征筛选。
第39次特征筛选，AUC值是： 0.5352035604844594
X_train_iter_svc.shape is: (1257, 13)
X_test_iter_svc.shape is: (1257, 13)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6979146054909721
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.697594607964383
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6979146054909721
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.697594607964383
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.665 (+/-0.225) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.309) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.660 (+/-0.290) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.384) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.167) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.601 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.646 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.595 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.646 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.167) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.188) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.576 (+/-0.207) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.731 (+/-0.365) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.622 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.596 (+/-0.193) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.593 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.581 (+/-0.201) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.707 (+/-0.416) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.620 (+/-0.290) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.594 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.583 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.671 (+/-0.375) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.289) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.593 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.583 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.235) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.676 (+/-0.244) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.301) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.654 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6991976873608706
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.652 (+/-0.313) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.642 (+/-0.204) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.310) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.204) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.185) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.598 (+/-0.298) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.642 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.182) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.625 (+/-0.282) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.384) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.182) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.597 (+/-0.161) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.605 (+/-0.311) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.182) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.597 (+/-0.161) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.196) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.622 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.597 (+/-0.161) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.583 (+/-0.190) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.580 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.697 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.225) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.604 (+/-0.176) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.600 (+/-0.165) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.589 (+/-0.194) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.581 (+/-0.201) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.831 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.535 (+/-0.144) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.277) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.591 (+/-0.188) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.583 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.551 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.276) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.588 (+/-0.194) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.583 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.315) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.694 (+/-0.305) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.657 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.694 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.600 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.694 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.643 (+/-0.209) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.706 (+/-0.339) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.576 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.301) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.365) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.633 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.07 0.33 0.12 6
1 0.99 0.96 0.98 623
avg / total 0.98 0.95 0.97 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7056069131832797
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.634 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.185) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.185) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.622 (+/-0.184) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.608 (+/-0.161) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.617 (+/-0.181) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.603 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.594 (+/-0.191) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.596 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.655 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.597 (+/-0.161) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.167) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.618 (+/-0.185) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.618 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.597 (+/-0.191) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.596 (+/-0.191) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.592 (+/-0.192) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.593 (+/-0.195) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.653 (+/-0.286) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.638 (+/-0.287) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.160) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.620 (+/-0.184) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.621 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.595 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.595 (+/-0.192) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.194) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.589 (+/-0.194) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.591 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.645 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.289) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.160) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.597 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.187) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.596 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.596 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.595 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.592 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.587 (+/-0.195) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.590 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.621 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.621 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.591 (+/-0.154) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.600 (+/-0.158) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.177) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.592 (+/-0.193) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.590 (+/-0.195) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.195) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.589 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.620 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.601 (+/-0.164) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.184) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.193) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.591 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.195) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.289) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.618 (+/-0.211) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.184) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.193) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.170) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.592 (+/-0.195) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.616 (+/-0.291) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.596 (+/-0.175) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.616 (+/-0.211) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.184) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.589 (+/-0.189) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.164) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.590 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.194) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.595 (+/-0.175) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.628 (+/-0.225) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.584 (+/-0.184) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.187) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.164) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.194) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.290) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.598 (+/-0.175) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.620 (+/-0.212) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.184) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.187) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.163) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.623 (+/-0.289) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.598 (+/-0.175) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.623 (+/-0.216) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.185) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.187) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.163) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.680 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.662 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.661 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.685 (+/-0.369) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.685 (+/-0.368) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.300) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.685 (+/-0.367) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.684 (+/-0.368) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.685 (+/-0.367) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.684 (+/-0.367) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.308) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.685 (+/-0.367) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.684 (+/-0.367) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.604 (+/-0.176) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.614 (+/-0.292) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.582 (+/-0.145) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.163) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.165) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.594 (+/-0.162) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.603 (+/-0.182) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.616 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.584 (+/-0.177) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.580 (+/-0.144) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.153) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.161) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.610 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.180) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.195) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.599 (+/-0.278) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.585 (+/-0.176) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.582 (+/-0.145) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.152) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.611 (+/-0.183) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.616 (+/-0.182) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.587 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.602 (+/-0.278) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.580 (+/-0.150) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.589 (+/-0.153) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.609 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.188) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.586 (+/-0.196) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.196) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.604 (+/-0.277) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.143) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.149) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.599 (+/-0.149) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.195) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.607 (+/-0.277) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.581 (+/-0.144) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.587 (+/-0.152) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.585 (+/-0.197) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.604 (+/-0.277) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.581 (+/-0.144) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.178) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.189) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.577 (+/-0.174) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.196) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.587 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.604 (+/-0.277) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.580 (+/-0.144) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.183) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.194) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.573 (+/-0.167) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.196) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.606 (+/-0.277) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.580 (+/-0.144) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.603 (+/-0.180) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.587 (+/-0.183) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.194) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.573 (+/-0.167) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.196) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.606 (+/-0.277) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.146) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.593 (+/-0.160) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.587 (+/-0.184) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.573 (+/-0.167) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.196) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.609 (+/-0.276) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.146) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.596 (+/-0.167) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.587 (+/-0.184) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.573 (+/-0.167) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.196) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.696 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.298) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.716 (+/-0.351) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.699 (+/-0.345) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.298) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.345) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.366) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.717 (+/-0.354) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.742 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.365) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.366) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.718 (+/-0.351) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.716 (+/-0.351) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.707 (+/-0.339) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.365) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.742 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.364) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.338) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.367) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.339) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.366) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.341) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.368) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.339) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.366) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.340) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.341) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.365) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.302) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.306) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.340) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.340) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.365) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.302) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.306) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.340) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.341) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.365) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.306) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.340) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.341) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.365) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.633 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.07 0.17 0.10 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7421448594278177
循环迭代之前，delta is: [3.69042656e+06 6.01892829e-06]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([3981071.70553497, 4365158.32240166, 4786300.92322638,
5248074.60249772, 5754399.37337156, 6309573.44480193,
6918309.70918936, 7585775.75029184, 8317637.7110267 ,
9120108.39355909, 9999999.99999999]), 'kernel': ['rbf'], 'gamma': array([2.51188643e-06, 2.75422870e-06, 3.01995172e-06, 3.31131121e-06,
3.63078055e-06, 3.98107171e-06, 4.36515832e-06, 4.78630092e-06,
5.24807460e-06, 5.75439937e-06, 6.30957344e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.595 (+/-0.160) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.651 (+/-0.296) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.594 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.636 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.165) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.176) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.648 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.626 (+/-0.188) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.621 (+/-0.187) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.597 (+/-0.161) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.651 (+/-0.296) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.596 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.632 (+/-0.286) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.165) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.596 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.602 (+/-0.177) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.656 (+/-0.292) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.626 (+/-0.188) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.604 (+/-0.161) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.154) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.649 (+/-0.296) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.594 (+/-0.155) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.633 (+/-0.287) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.598 (+/-0.160) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.158) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.607 (+/-0.180) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.645 (+/-0.287) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.626 (+/-0.188) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.604 (+/-0.161) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.649 (+/-0.296) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.592 (+/-0.155) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.287) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.596 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.610 (+/-0.180) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.645 (+/-0.287) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.627 (+/-0.197) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.603 (+/-0.162) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.160) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.652 (+/-0.295) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.593 (+/-0.155) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.643 (+/-0.285) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.600 (+/-0.158) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.607 (+/-0.180) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.639 (+/-0.288) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.596 (+/-0.192) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.622 (+/-0.186) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.603 (+/-0.162) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.154) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.652 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.593 (+/-0.155) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.649 (+/-0.287) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.600 (+/-0.158) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.639 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.596 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.591 (+/-0.177) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.154) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.652 (+/-0.295) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.592 (+/-0.155) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.646 (+/-0.287) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.600 (+/-0.158) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.192) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.628 (+/-0.300) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.598 (+/-0.192) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.589 (+/-0.174) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.160) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.644 (+/-0.288) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.597 (+/-0.155) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.638 (+/-0.280) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.151) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.192) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.603 (+/-0.196) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.598 (+/-0.192) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.597 (+/-0.160) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.599 (+/-0.158) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.636 (+/-0.280) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.612 (+/-0.186) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.174) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.191) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.612 (+/-0.225) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.164) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.599 (+/-0.158) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.638 (+/-0.280) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.612 (+/-0.186) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.174) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.191) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.606 (+/-0.196) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.594 (+/-0.191) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.164) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.600 (+/-0.159) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.636 (+/-0.280) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.612 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.585 (+/-0.184) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.610 (+/-0.199) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.594 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.678 (+/-0.288) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.294) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.696 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.302) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.288) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.295) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.293) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.307) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.302) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.288) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.295) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.293) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.307) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.289) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.308) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.295) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.305) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.696 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.288) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.680 (+/-0.296) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.295) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.293) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.680 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.305) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.696 (+/-0.304) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.317) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.663 (+/-0.310) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.313) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.696 (+/-0.303) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.305) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.679 (+/-0.291) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.316) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.663 (+/-0.310) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.696 (+/-0.303) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.305) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.678 (+/-0.289) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.317) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.696 (+/-0.303) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.306) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.678 (+/-0.289) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.316) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.316) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.696 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.680 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.661 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.589 (+/-0.153) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.171) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.630 (+/-0.280) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.592 (+/-0.161) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.177) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.166) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.594 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.181) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.609 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.590 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.171) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.625 (+/-0.279) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.590 (+/-0.162) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.178) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.608 (+/-0.186) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.592 (+/-0.192) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.604 (+/-0.182) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.162) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.590 (+/-0.155) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.169) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.621 (+/-0.280) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.589 (+/-0.163) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.596 (+/-0.159) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.183) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.597 (+/-0.166) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.592 (+/-0.192) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.604 (+/-0.182) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.162) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.590 (+/-0.155) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.603 (+/-0.162) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.586 (+/-0.155) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.626 (+/-0.282) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.594 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.597 (+/-0.166) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.604 (+/-0.182) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.163) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.145) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.586 (+/-0.155) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.632 (+/-0.282) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.149) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.181) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.590 (+/-0.160) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.604 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.163) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.586 (+/-0.149) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.587 (+/-0.155) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.284) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.149) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.589 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.590 (+/-0.193) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.145) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.164) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.586 (+/-0.155) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.149) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.192) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.578 (+/-0.170) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.589 (+/-0.193) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.152) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.164) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.587 (+/-0.155) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.590 (+/-0.141) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.193) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.577 (+/-0.170) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.193) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.589 (+/-0.193) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.588 (+/-0.194) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.152) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.590 (+/-0.159) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.598 (+/-0.166) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.589 (+/-0.192) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.577 (+/-0.170) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.193) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.589 (+/-0.193) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.588 (+/-0.194) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.151) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.590 (+/-0.159) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.166) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.192) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.579 (+/-0.172) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.587 (+/-0.192) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.589 (+/-0.193) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.588 (+/-0.194) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.152) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.593 (+/-0.160) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.627 (+/-0.282) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.587 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.584 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.579 (+/-0.172) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.588 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.592 (+/-0.188) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.694 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.694 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.299) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.351) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.292) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.368) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.700 (+/-0.347) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.716 (+/-0.351) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.300) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.299) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.352) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.293) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.367) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.700 (+/-0.346) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.699 (+/-0.343) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.307) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.300) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.694 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.351) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.675 (+/-0.291) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.366) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.700 (+/-0.346) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.699 (+/-0.343) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.306) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.299) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.308) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.717 (+/-0.350) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.700 (+/-0.347) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.366) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.699 (+/-0.347) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.698 (+/-0.343) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.299) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.309) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.742 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.700 (+/-0.347) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.293) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.366) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.699 (+/-0.347) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.698 (+/-0.343) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.742 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.368) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.293) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.365) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.682 (+/-0.367) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.364) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.300) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.310) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.742 (+/-0.315) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.368) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.659 (+/-0.312) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.365) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.682 (+/-0.367) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.301) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.310) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.367) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.312) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.364) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.682 (+/-0.367) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.694 (+/-0.311) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.299) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.311) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.364) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.682 (+/-0.366) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.694 (+/-0.311) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.311) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.365) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.681 (+/-0.367) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.680 (+/-0.367) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.678 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.708 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.366) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.706 (+/-0.340) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.680 (+/-0.367) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.633 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.07 0.17 0.10 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7421448594278177
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([5248074.60249772, 5345643.5939697 , 5445026.5284242 ,
5546257.1295791 , 5649369.74812302, 5754399.37337156,
5861381.64514028, 5970352.86583836, 6081350.01278718,
6194410.75076781, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([3.63078055e-06, 3.69828180e-06, 3.76703799e-06, 3.83707245e-06,
3.90840896e-06, 3.98107171e-06, 4.05508535e-06, 4.13047502e-06,
4.20726628e-06, 4.28548520e-06, 4.36515832e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}, 0.7421448594278177)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [555174.07143037 0. ]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([5248074.60249772, 5345643.5939697 , 5445026.5284242 ,
5546257.1295791 , 5649369.74812302, 5754399.37337156,
5861381.64514028, 5970352.86583836, 6081350.01278718,
6194410.75076781, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([3.63078055e-06, 3.69828180e-06, 3.76703799e-06, 3.83707245e-06,
3.90840896e-06, 3.98107171e-06, 4.05508535e-06, 4.13047502e-06,
4.20726628e-06, 4.28548520e-06, 4.36515832e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.609 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.601 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.601 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.596 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.645 (+/-0.294) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.604 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.622 (+/-0.198) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.610 (+/-0.180) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.601 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.601 (+/-0.160) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.645 (+/-0.294) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.604 (+/-0.160) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.610 (+/-0.180) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.161) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.602 (+/-0.160) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.598 (+/-0.158) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.648 (+/-0.293) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.604 (+/-0.160) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.609 (+/-0.180) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.161) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.162) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.602 (+/-0.160) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.648 (+/-0.293) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.604 (+/-0.160) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.607 (+/-0.180) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.162) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.648 (+/-0.293) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.164) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.607 (+/-0.180) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.648 (+/-0.293) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.607 (+/-0.180) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.610 (+/-0.164) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.635 (+/-0.307) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.164) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.181) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.610 (+/-0.164) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.635 (+/-0.307) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.164) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.620 (+/-0.238) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.592 (+/-0.192) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.177) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.635 (+/-0.307) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.627 (+/-0.210) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.618 (+/-0.238) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.592 (+/-0.192) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.596 (+/-0.157) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.177) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.635 (+/-0.307) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.627 (+/-0.210) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.609 (+/-0.212) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.592 (+/-0.192) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.612 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.596 (+/-0.157) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.635 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.592 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.695 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.694 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.695 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.695 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.296) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.680 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.680 (+/-0.295) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.694 (+/-0.298) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.695 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.296) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.680 (+/-0.293) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.694 (+/-0.298) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.681 (+/-0.296) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.680 (+/-0.293) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.681 (+/-0.296) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.680 (+/-0.293) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.681 (+/-0.296) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.681 (+/-0.296) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.293) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.664 (+/-0.317) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.293) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.664 (+/-0.317) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.664 (+/-0.316) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.662 (+/-0.308) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.664 (+/-0.317) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.697 (+/-0.308) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.662 (+/-0.308) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.664 (+/-0.317) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.697 (+/-0.308) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.315) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.662 (+/-0.308) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.664 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.662 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.599 (+/-0.186) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.595 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.593 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.594 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.597 (+/-0.178) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.590 (+/-0.154) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.600 (+/-0.180) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.606 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.591 (+/-0.164) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.594 (+/-0.162) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.592 (+/-0.160) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.594 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.597 (+/-0.178) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.590 (+/-0.154) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.590 (+/-0.191) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.606 (+/-0.181) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.161) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.593 (+/-0.162) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.591 (+/-0.161) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.148) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.604 (+/-0.176) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.589 (+/-0.154) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.590 (+/-0.191) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.181) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.591 (+/-0.165) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.593 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.593 (+/-0.162) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.591 (+/-0.161) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.604 (+/-0.176) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.588 (+/-0.154) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.181) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.593 (+/-0.163) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.591 (+/-0.161) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.604 (+/-0.176) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.606 (+/-0.181) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.591 (+/-0.161) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.604 (+/-0.176) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.606 (+/-0.181) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.589 (+/-0.160) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.162) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.590 (+/-0.187) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.181) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.589 (+/-0.160) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.162) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.187) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.593 (+/-0.192) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.589 (+/-0.160) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.187) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.593 (+/-0.192) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.589 (+/-0.160) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.590 (+/-0.187) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.593 (+/-0.192) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.605 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.589 (+/-0.160) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.590 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.593 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.692 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.694 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.349) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.717 (+/-0.350) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.677 (+/-0.292) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.677 (+/-0.292) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.677 (+/-0.292) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.347) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.694 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.349) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.717 (+/-0.350) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.677 (+/-0.292) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.677 (+/-0.292) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.661 (+/-0.312) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.347) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.301) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.349) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.702 (+/-0.346) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.677 (+/-0.292) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.661 (+/-0.312) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.346) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.301) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.349) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.702 (+/-0.346) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.677 (+/-0.291) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.661 (+/-0.312) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.346) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.301) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.314) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.702 (+/-0.346) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.661 (+/-0.312) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.347) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.702 (+/-0.346) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.347) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.303) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.314) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.290) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.685 (+/-0.367) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.347) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.303) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.314) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.291) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.685 (+/-0.368) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.683 (+/-0.368) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.303) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.310) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.685 (+/-0.368) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.683 (+/-0.368) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.303) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.685 (+/-0.367) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.683 (+/-0.368) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.685 (+/-0.367) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.683 (+/-0.368) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.633 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.07 0.17 0.10 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7424653722483305
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([5345643.5939697 , 5365373.99519851, 5385177.21997526,
5405053.53708668, 5425003.21631149, 5445026.5284242 ,
5465123.74519871, 5485295.13941203, 5505540.98484794,
5525861.55630077, 5546257.1295791 ]), 'kernel': ['rbf'], 'gamma': array([3.90840896e-06, 3.92283463e-06, 3.93731354e-06, 3.95184589e-06,
3.96643188e-06, 3.98107171e-06, 3.99576557e-06, 4.01051366e-06,
4.02531619e-06, 4.04017335e-06, 4.05508535e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}, 0.7424653722483305)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [309372.84494736 0. ]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([5345643.5939697 , 5365373.99519851, 5385177.21997526,
5405053.53708668, 5425003.21631149, 5445026.5284242 ,
5465123.74519871, 5485295.13941203, 5505540.98484794,
5525861.55630077, 5546257.1295791 ]), 'kernel': ['rbf'], 'gamma': array([3.90840896e-06, 3.92283463e-06, 3.93731354e-06, 3.95184589e-06,
3.96643188e-06, 3.98107171e-06, 3.99576557e-06, 4.01051366e-06,
4.02531619e-06, 4.04017335e-06, 4.05508535e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.605 (+/-0.163) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.163) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.605 (+/-0.179) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.604 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.619 (+/-0.187) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.161) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.161) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.161) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.161) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.163) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.619 (+/-0.187) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.163) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.605 (+/-0.179) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.619 (+/-0.187) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.161) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.160) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.607 (+/-0.161) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.160) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.619 (+/-0.187) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.607 (+/-0.161) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.160) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.619 (+/-0.181) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.607 (+/-0.161) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.159) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.696 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.695 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.696 (+/-0.304) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.294) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.294) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.695 (+/-0.303) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.294) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.303) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.303) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.303) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.294) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.303) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.598 (+/-0.165) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.151) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.605 (+/-0.180) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.594 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.606 (+/-0.146) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.598 (+/-0.175) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.600 (+/-0.140) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.600 (+/-0.140) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.600 (+/-0.140) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.598 (+/-0.175) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.600 (+/-0.140) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.151) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.151) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.604 (+/-0.180) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.594 (+/-0.168) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.599 (+/-0.149) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.594 (+/-0.168) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.603 (+/-0.165) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.599 (+/-0.149) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.158) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.718 (+/-0.349) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.318) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.718 (+/-0.352) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.717 (+/-0.350) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.719 (+/-0.353) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.744 (+/-0.319) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.702 (+/-0.345) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.719 (+/-0.353) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.345) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.719 (+/-0.353) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.345) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.719 (+/-0.353) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.702 (+/-0.345) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.719 (+/-0.353) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.318) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.345) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.318) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.718 (+/-0.351) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.344) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.692 (+/-0.301) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.344) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.344) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.692 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.726 (+/-0.314) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.633 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.06 0.17 0.08 6
1 0.99 0.97 0.98 623
avg / total 0.98 0.97 0.97 629
本轮grid search结果，得到最好的参数选择是： {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7437474235303818
发现最优参数C为原先的最大/最小值，直接重新设置超参。
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([5.34564359e+01, 5.34564359e+02, 5.34564359e+03, 5.34564359e+04,
5.34564359e+05, 5.34564359e+06, 5.34564359e+07, 5.34564359e+08,
5.34564359e+09, 5.34564359e+10, 5.34564359e+11]), 'kernel': ['rbf'], 'gamma': array([3.99576557e-06, 3.99871084e-06, 4.00165828e-06, 4.00460790e-06,
4.00755969e-06, 4.01051366e-06, 4.01346981e-06, 4.01642813e-06,
4.01938863e-06, 4.02235132e-06, 4.02531619e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}, 0.7437474235303818)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [9.93829345e+04 2.94419553e-08]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 4.01051366086576e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 5345643.593969704, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9518072289156626
测试集中，预测为舞弊样本的有： (array([ 0, 1, 2, ..., 1254, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 1076
训练模型SVC对测试样本的预测准确率： 0.23387668320340185
以上是第40次特征筛选。
第40次特征筛选，AUC值是： 0.5267765941923246
X_train_iter_svc.shape is: (1257, 12)
X_test_iter_svc.shape is: (1257, 12)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.609 (+/-0.309) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.585 (+/-0.188) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6982356336054085
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6982356336054085
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.685 (+/-0.297) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.644 (+/-0.285) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.608 (+/-0.309) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.184) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.597 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.219) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.608 (+/-0.185) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.593 (+/-0.188) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.604 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.219) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.599 (+/-0.192) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.201) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.651 (+/-0.211) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.183) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.591 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.576 (+/-0.196) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.703 (+/-0.418) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.291) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.595 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.578 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.435) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.592 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.580 (+/-0.188) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.196) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.578 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.607 (+/-0.182) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.189) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.696 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.696 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.235) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.302) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.258) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.633 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.301) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.324) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6991982026547944
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.669 (+/-0.296) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.667 (+/-0.292) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.384) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.204) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.633 (+/-0.197) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.593 (+/-0.296) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.642 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.624 (+/-0.186) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.618 (+/-0.278) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.608 (+/-0.309) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.624 (+/-0.186) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.153) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.601 (+/-0.311) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.186) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.153) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.580 (+/-0.188) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.604 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.631 (+/-0.197) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.155) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.576 (+/-0.197) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.647 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.606 (+/-0.180) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.590 (+/-0.158) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.585 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.576 (+/-0.196) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.542 (+/-0.143) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.599 (+/-0.282) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.591 (+/-0.188) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.580 (+/-0.188) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.578 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.553 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.608 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.591 (+/-0.188) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.580 (+/-0.188) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.196) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.578 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.584 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.585 (+/-0.188) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.294) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.012) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.694 (+/-0.305) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.693 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.692 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.657 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.657 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.608 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.256) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.301) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.705 (+/-0.341) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.607 (+/-0.162) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.705 (+/-0.340) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.324) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.07 0.33 0.12 6
1 0.99 0.96 0.98 623
avg / total 0.98 0.95 0.97 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.705286400362767
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.651 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.350) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.634 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.620 (+/-0.183) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.176) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.610 (+/-0.183) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.159) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.617 (+/-0.187) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.601 (+/-0.198) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.595 (+/-0.188) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.591 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.639 (+/-0.202) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.160) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.594 (+/-0.157) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.605 (+/-0.165) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.182) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.185) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.594 (+/-0.190) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.591 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.658 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.602 (+/-0.182) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.593 (+/-0.155) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.625 (+/-0.211) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.183) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.606 (+/-0.176) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.596 (+/-0.193) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.589 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.589 (+/-0.191) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.649 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.288) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.597 (+/-0.161) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.599 (+/-0.180) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.614 (+/-0.179) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.197) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.195) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.187) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.189) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.584 (+/-0.187) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.638 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.625 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.601 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.600 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.619 (+/-0.181) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.587 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.583 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.621 (+/-0.291) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.288) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.603 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.599 (+/-0.175) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.587 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.581 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.616 (+/-0.292) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.593 (+/-0.175) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.611 (+/-0.185) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.172) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.582 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.616 (+/-0.292) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.594 (+/-0.176) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.603 (+/-0.177) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.197) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.172) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.189) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.293) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.592 (+/-0.176) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.186) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.587 (+/-0.172) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.173) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.186) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.190) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.189) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.614 (+/-0.293) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.595 (+/-0.175) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.605 (+/-0.209) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.186) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.587 (+/-0.172) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.192) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.173) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.579 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.186) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.190) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.614 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.595 (+/-0.174) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.608 (+/-0.209) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.173) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.192) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.172) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.579 (+/-0.188) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.584 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.190) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.607 (+/-0.182) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.189) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.696 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.289) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.679 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.294) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.318) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.696 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.679 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.661 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.679 (+/-0.292) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.679 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.304) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.679 (+/-0.292) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.683 (+/-0.367) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.300) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.658 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.683 (+/-0.367) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.658 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.683 (+/-0.366) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.658 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.683 (+/-0.365) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.606 (+/-0.180) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.645 (+/-0.363) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.576 (+/-0.141) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.585 (+/-0.145) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.590 (+/-0.158) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.585 (+/-0.162) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.602 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.190) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.578 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.610 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.178) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.581 (+/-0.147) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.581 (+/-0.144) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.146) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.597 (+/-0.160) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.187) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.190) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.581 (+/-0.191) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.582 (+/-0.190) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.591 (+/-0.281) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.586 (+/-0.177) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.587 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.152) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.182) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.593 (+/-0.184) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.191) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.582 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.594 (+/-0.282) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.579 (+/-0.143) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.591 (+/-0.156) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.596 (+/-0.181) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.178) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.187) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.585 (+/-0.196) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.190) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.594 (+/-0.281) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.175) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.596 (+/-0.182) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.175) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.580 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.581 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.581 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.599 (+/-0.282) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.584 (+/-0.144) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.591 (+/-0.175) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.591 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.589 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.585 (+/-0.196) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.580 (+/-0.189) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.602 (+/-0.281) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.580 (+/-0.144) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.599 (+/-0.181) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.578 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.178) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.174) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.189) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.581 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.608 (+/-0.287) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.145) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.175) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.578 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.178) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.174) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.189) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.582 (+/-0.187) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.608 (+/-0.287) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.580 (+/-0.144) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.185) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.578 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.150) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.174) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.189) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.186) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.189) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.609 (+/-0.286) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.583 (+/-0.144) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.585 (+/-0.185) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.150) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.174) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.189) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.186) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.608 (+/-0.286) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.144) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.188) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.150) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.174) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.189) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.584 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.585 (+/-0.188) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.696 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.715 (+/-0.351) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.698 (+/-0.344) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.363) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.717 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.699 (+/-0.344) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.671 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.297) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.699 (+/-0.345) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.707 (+/-0.340) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.724 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.341) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.677 (+/-0.292) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.676 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.706 (+/-0.342) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.300) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.731 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.340) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.303) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.677 (+/-0.293) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.731 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.341) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.365) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.304) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.301) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.730 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.340) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.366) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.730 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.340) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.366) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.730 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.340) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.366) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.05 0.17 0.07 6
1 0.99 0.97 0.98 623
avg / total 0.98 0.96 0.97 629
本轮grid search结果，得到最好的参数选择是： {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7309279412977162
循环迭代之前，delta is: [5.84893192e+06 3.69042656e-06]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([ 9999999.99999999, 10964781.96143185, 12022644.34617414,
13182567.38556406, 14454397.70745927, 15848931.92461113,
17378008.28749376, 19054607.17963249, 20892961.30854038,
22908676.52767773, 25118864.31509581]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-06, 4.36515832e-06, 4.78630092e-06, 5.24807460e-06,
5.75439937e-06, 6.30957344e-06, 6.91830971e-06, 7.58577575e-06,
8.31763771e-06, 9.12010839e-06, 1.00000000e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.599 (+/-0.175) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.589 (+/-0.176) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.183) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.585 (+/-0.170) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.587 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.577 (+/-0.162) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.582 (+/-0.184) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.175) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.183) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.581 (+/-0.164) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.604 (+/-0.193) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.587 (+/-0.187) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.577 (+/-0.162) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.585 (+/-0.186) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.175) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.183) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.581 (+/-0.164) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.601 (+/-0.193) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.587 (+/-0.187) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.575 (+/-0.162) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.585 (+/-0.186) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.588 (+/-0.186) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.183) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.581 (+/-0.164) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.193) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.191) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.586 (+/-0.188) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.576 (+/-0.162) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.585 (+/-0.186) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.588 (+/-0.186) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.579 (+/-0.164) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.183) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.580 (+/-0.164) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.193) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.191) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.586 (+/-0.188) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.575 (+/-0.162) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.585 (+/-0.186) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.603 (+/-0.196) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.577 (+/-0.162) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.598 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.575 (+/-0.162) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.582 (+/-0.184) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.603 (+/-0.196) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.577 (+/-0.162) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.201) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.598 (+/-0.194) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.188) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.573 (+/-0.161) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.582 (+/-0.184) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.603 (+/-0.196) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.581 (+/-0.168) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.201) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.598 (+/-0.194) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.188) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.573 (+/-0.161) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.583 (+/-0.184) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.603 (+/-0.196) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.581 (+/-0.168) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.201) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.601 (+/-0.197) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.577 (+/-0.164) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.573 (+/-0.161) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.583 (+/-0.184) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.191) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.579 (+/-0.164) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.601 (+/-0.195) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.577 (+/-0.162) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.201) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.601 (+/-0.197) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.576 (+/-0.165) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.572 (+/-0.162) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.583 (+/-0.184) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.191) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.579 (+/-0.164) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.601 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.577 (+/-0.162) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.201) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.600 (+/-0.197) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.576 (+/-0.165) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.572 (+/-0.162) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.583 (+/-0.184) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.191) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.607 (+/-0.182) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.189) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.679 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.661 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.661 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.661 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.679 (+/-0.290) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.315) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.660 (+/-0.310) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.661 (+/-0.310) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.312) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.679 (+/-0.290) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.315) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.661 (+/-0.311) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.660 (+/-0.308) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.661 (+/-0.310) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.660 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.661 (+/-0.310) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.660 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.660 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.659 (+/-0.308) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.659 (+/-0.307) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.316) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.659 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.659 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.312) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.316) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.659 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.659 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.659 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.659 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.591 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.169) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.573 (+/-0.163) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.575 (+/-0.171) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.589 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.581 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.569 (+/-0.165) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.578 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.593 (+/-0.179) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.571 (+/-0.164) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.187) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.580 (+/-0.192) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.569 (+/-0.165) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.581 (+/-0.187) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.180) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.571 (+/-0.164) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.580 (+/-0.191) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.569 (+/-0.165) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.581 (+/-0.187) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.189) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.571 (+/-0.164) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.580 (+/-0.192) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.569 (+/-0.165) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.581 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.188) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.569 (+/-0.163) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.187) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.580 (+/-0.192) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.570 (+/-0.165) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.581 (+/-0.187) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.569 (+/-0.163) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.583 (+/-0.189) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.570 (+/-0.165) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.578 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.569 (+/-0.163) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.583 (+/-0.189) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.568 (+/-0.164) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.579 (+/-0.186) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.573 (+/-0.170) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.583 (+/-0.189) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.568 (+/-0.164) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.579 (+/-0.186) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.574 (+/-0.162) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.574 (+/-0.170) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.575 (+/-0.165) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.568 (+/-0.164) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.579 (+/-0.186) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.574 (+/-0.162) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.569 (+/-0.161) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.569 (+/-0.163) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.575 (+/-0.165) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.568 (+/-0.164) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.579 (+/-0.186) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.574 (+/-0.162) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.569 (+/-0.161) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.569 (+/-0.163) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.575 (+/-0.165) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.568 (+/-0.164) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.579 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.592 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.584 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.585 (+/-0.188) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.676 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.365) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.657 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.369) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.706 (+/-0.342) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.655 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.654 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.680 (+/-0.363) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.676 (+/-0.290) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.657 (+/-0.310) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.370) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.705 (+/-0.344) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.655 (+/-0.310) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.654 (+/-0.311) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.363) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.341) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.675 (+/-0.291) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.657 (+/-0.311) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.370) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.730 (+/-0.313) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.655 (+/-0.311) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.363) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.310) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.370) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.706 (+/-0.342) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.654 (+/-0.311) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.654 (+/-0.312) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.363) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.310) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.371) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.310) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.654 (+/-0.311) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.654 (+/-0.313) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.363) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.370) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.365) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.654 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.680 (+/-0.363) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.313) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.370) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.343) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.365) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.680 (+/-0.364) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.313) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.310) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.370) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.343) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.680 (+/-0.365) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.364) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.370) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.343) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.680 (+/-0.364) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.364) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.310) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.371) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.343) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.680 (+/-0.364) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.680 (+/-0.364) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.367) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.656 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.371) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.343) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.680 (+/-0.364) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.680 (+/-0.364) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.341) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.05 0.17 0.07 6
1 0.99 0.97 0.98 623
avg / total 0.98 0.96 0.97 629
本轮grid search结果，得到最好的参数选择是： {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7310881977079726
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([13182567.38556406, 13427649.61137862, 13677288.25595846,
13931568.02945303, 14190575.21689092, 14454397.70745927,
14723125.02432717, 14996848.35502371, 15275660.58238074,
15559656.31605075, 15848931.92461113]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-06, 5.86138165e-06, 5.97035287e-06, 6.08135001e-06,
6.19441075e-06, 6.30957344e-06, 6.42687717e-06, 6.54636174e-06,
6.66806769e-06, 6.79203633e-06, 6.91830971e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.7310881977079726)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [1394534.21715187 0. ]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([13182567.38556406, 13427649.61137862, 13677288.25595846,
13931568.02945303, 14190575.21689092, 14454397.70745927,
14723125.02432717, 14996848.35502371, 15275660.58238074,
15559656.31605075, 15848931.92461113]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-06, 5.86138165e-06, 5.97035287e-06, 6.08135001e-06,
6.19441075e-06, 6.30957344e-06, 6.42687717e-06, 6.54636174e-06,
6.66806769e-06, 6.79203633e-06, 6.91830971e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.603 (+/-0.201) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.585 (+/-0.170) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.177) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.600 (+/-0.197) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.587 (+/-0.170) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.177) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.600 (+/-0.197) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.587 (+/-0.170) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.177) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.600 (+/-0.197) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.585 (+/-0.170) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.177) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.587 (+/-0.170) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.584 (+/-0.169) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.586 (+/-0.185) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.584 (+/-0.169) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.584 (+/-0.169) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.584 (+/-0.169) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.587 (+/-0.170) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.587 (+/-0.170) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.598 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.607 (+/-0.182) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.189) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.315) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.660 (+/-0.309) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.315) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.660 (+/-0.311) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.660 (+/-0.309) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.660 (+/-0.311) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.662 (+/-0.316) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.660 (+/-0.309) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.660 (+/-0.312) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.662 (+/-0.316) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.660 (+/-0.309) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.315) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.662 (+/-0.315) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.660 (+/-0.312) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.660 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.662 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.660 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.585 (+/-0.192) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.568 (+/-0.155) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.593 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.588 (+/-0.194) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.570 (+/-0.156) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.593 (+/-0.187) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.588 (+/-0.194) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.593 (+/-0.187) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.588 (+/-0.194) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.567 (+/-0.155) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.593 (+/-0.187) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.189) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.593 (+/-0.187) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.189) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.578 (+/-0.183) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.589 (+/-0.190) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.589 (+/-0.190) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.589 (+/-0.190) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.589 (+/-0.190) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.584 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.585 (+/-0.188) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.680 (+/-0.370) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.706 (+/-0.342) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.341) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.364) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.370) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.703 (+/-0.336) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.706 (+/-0.342) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.682 (+/-0.365) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.370) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.706 (+/-0.342) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.341) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.682 (+/-0.365) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.333) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.706 (+/-0.342) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.364) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.706 (+/-0.342) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.364) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.310) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.310) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.310) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.368) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.290) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.310) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.705 (+/-0.339) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.288) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.290) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.311) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.370) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.705 (+/-0.339) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.370) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.288) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.370) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.705 (+/-0.339) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.05 0.17 0.07 6
1 0.99 0.97 0.98 623
avg / total 0.98 0.96 0.97 629
本轮grid search结果，得到最好的参数选择是： {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7310881977079726
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([14190575.21689092, 14242951.64973292, 14295521.40037034,
14348285.18232589, 14401243.71175568, 14454397.70745927,
14507747.89088912, 14561294.98616055, 14615039.72006164,
14668982.82206284, 14723125.02432717]), 'kernel': ['rbf'], 'gamma': array([6.19441075e-06, 6.21727389e-06, 6.24022142e-06, 6.26325365e-06,
6.28637088e-06, 6.30957344e-06, 6.33286164e-06, 6.35623580e-06,
6.37969623e-06, 6.40324324e-06, 6.42687717e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.7310881977079726)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [0. 0.]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 6.309573444801928e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 14454397.707459265, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9539333805811481
测试集中，预测为舞弊样本的有： (array([ 0, 1, 2, ..., 1254, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 1150
训练模型SVC对测试样本的预测准确率： 0.18143160878809356
以上是第41次特征筛选。
第41次特征筛选，AUC值是： 0.4970815701152779
X_train_iter_svc.shape is: (1257, 11)
X_test_iter_svc.shape is: (1257, 11)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.604 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.581 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6982356336054085
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6982356336054085
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.685 (+/-0.297) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.637 (+/-0.286) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.183) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.219) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.601 (+/-0.179) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.187) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.219) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.604 (+/-0.182) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.567 (+/-0.197) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.602 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.597 (+/-0.211) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.186) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.192) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.703 (+/-0.418) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.628 (+/-0.288) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.586 (+/-0.187) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.435) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.570 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.604 (+/-0.181) for {'C': 10.0, 'kernel': 'linear'}
0.609 (+/-0.201) for {'C': 100.0, 'kernel': 'linear'}
0.584 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.577 (+/-0.192) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.696 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.696 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.234) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.258) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.633 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.302) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6991982026547944
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.669 (+/-0.296) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.667 (+/-0.292) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.384) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.204) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.199) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.591 (+/-0.295) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.642 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.624 (+/-0.186) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.280) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.624 (+/-0.186) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.582 (+/-0.153) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.594 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.186) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.146) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.578 (+/-0.187) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.186) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.146) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.186) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.573 (+/-0.192) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.647 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.197) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.584 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.187) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.186) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.192) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.542 (+/-0.143) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.593 (+/-0.280) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.189) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.553 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.568 (+/-0.155) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.582 (+/-0.190) for {'C': 100.0, 'kernel': 'linear'}
0.581 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.575 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.294) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.012) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.693 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.693 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.692 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.657 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.692 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.608 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.655 (+/-0.246) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.364) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.604 (+/-0.160) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.304) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.363) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.602 (+/-0.182) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.597 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.186) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.723 (+/-0.359) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.672 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.598 (+/-0.164) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.586 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.186) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.653 (+/-0.286) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.611 (+/-0.183) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.579 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.353) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.649 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.597 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.579 (+/-0.186) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.710 (+/-0.406) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.645 (+/-0.287) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.587 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.703 (+/-0.418) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.628 (+/-0.288) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.586 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.703 (+/-0.418) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.624 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.586 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.372) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.624 (+/-0.290) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.584 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.715 (+/-0.415) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.624 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.570 (+/-0.154) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.740 (+/-0.449) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.290) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.570 (+/-0.154) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.435) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.290) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.570 (+/-0.154) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.604 (+/-0.181) for {'C': 10.0, 'kernel': 'linear'}
0.609 (+/-0.201) for {'C': 100.0, 'kernel': 'linear'}
0.584 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.577 (+/-0.192) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.318) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.697 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.324) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.258) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.221) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.219) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.218) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.219) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.308) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.633 (+/-0.237) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.302) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6995187154753071
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.647 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.231) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.197) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.584 (+/-0.154) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.187) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.186) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.640 (+/-0.209) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.590 (+/-0.158) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.187) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.186) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.626 (+/-0.188) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.593 (+/-0.280) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.600 (+/-0.182) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.574 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.600 (+/-0.216) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.591 (+/-0.283) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.588 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.186) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.555 (+/-0.143) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.592 (+/-0.280) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.584 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.647 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.543 (+/-0.144) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.593 (+/-0.280) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.189) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.536 (+/-0.144) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.608 (+/-0.287) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.189) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.529 (+/-0.146) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.286) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.580 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.554 (+/-0.296) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.605 (+/-0.283) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.568 (+/-0.155) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.554 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.605 (+/-0.283) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.568 (+/-0.155) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.647 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.553 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.286) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.568 (+/-0.155) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.582 (+/-0.190) for {'C': 100.0, 'kernel': 'linear'}
0.581 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.575 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.575 (+/-0.229) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.697 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.297) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.716 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.697 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.699 (+/-0.344) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.692 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.296) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.365) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.678 (+/-0.277) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.364) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.324) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.575 (+/-0.229) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.248) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.364) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.652 (+/-0.183) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.304) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.364) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.612 (+/-0.143) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.304) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.364) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.616 (+/-0.170) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.363) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.615 (+/-0.177) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.363) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.308) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.575 (+/-0.229) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.610 (+/-0.170) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.304) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.363) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.04 0.17 0.06 6
1 0.99 0.96 0.98 623
avg / total 0.98 0.95 0.97 629
本轮grid search结果，得到最好的参数选择是： {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7161833209662792
循环迭代之前，delta is: [8.41510681e+06 9.99000000e-06]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1000000. , 1096478.19614319, 1202264.43461741,
1318256.73855641, 1445439.77074593, 1584893.19246111,
1737800.82874938, 1905460.71796325, 2089296.13085404,
2290867.65276777, 2511886.43150958]), 'kernel': ['rbf'], 'gamma': array([1.00000000e-06, 1.58489319e-06, 2.51188643e-06, 3.98107171e-06,
6.30957344e-06, 1.00000000e-05, 1.58489319e-05, 2.51188643e-05,
3.98107171e-05, 6.30957344e-05, 1.00000000e-04])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.350) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.635 (+/-0.198) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.620 (+/-0.183) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.154) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.602 (+/-0.182) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.594 (+/-0.159) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.608 (+/-0.177) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.191) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.187) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.597 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.660 (+/-0.219) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.668 (+/-0.290) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.635 (+/-0.198) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.610 (+/-0.179) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.602 (+/-0.182) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.594 (+/-0.159) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.610 (+/-0.180) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.598 (+/-0.192) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.186) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.591 (+/-0.198) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.660 (+/-0.219) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.202) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.633 (+/-0.200) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.180) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.177) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.161) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.164) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.598 (+/-0.192) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.186) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.192) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.676 (+/-0.294) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.668 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.633 (+/-0.200) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.604 (+/-0.181) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.177) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.162) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.599 (+/-0.164) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.609 (+/-0.201) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.591 (+/-0.187) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.187) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.188) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.672 (+/-0.295) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.668 (+/-0.290) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.288) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.594 (+/-0.160) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.177) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.164) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.608 (+/-0.183) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.609 (+/-0.201) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.186) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.187) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.188) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.672 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.202) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.622 (+/-0.185) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.593 (+/-0.159) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.177) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.164) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.604 (+/-0.180) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.610 (+/-0.209) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.186) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.291) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.202) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.621 (+/-0.186) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.594 (+/-0.160) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.177) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.164) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.602 (+/-0.180) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.608 (+/-0.209) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.187) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.186) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.583 (+/-0.187) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.291) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.292) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.618 (+/-0.184) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.160) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.177) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.164) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.191) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.606 (+/-0.210) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.187) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.187) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.581 (+/-0.188) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.653 (+/-0.286) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.292) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.641 (+/-0.287) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.160) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.176) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.183) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.190) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.603 (+/-0.210) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.186) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.187) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.581 (+/-0.188) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.655 (+/-0.286) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.292) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.637 (+/-0.289) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.591 (+/-0.154) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.176) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.184) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.190) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.608 (+/-0.213) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.187) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.187) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.187) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.653 (+/-0.286) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.606 (+/-0.181) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.589 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.183) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.594 (+/-0.191) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.601 (+/-0.202) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.584 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.188) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.604 (+/-0.181) for {'C': 10.0, 'kernel': 'linear'}
0.609 (+/-0.201) for {'C': 100.0, 'kernel': 'linear'}
0.584 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.577 (+/-0.192) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.679 (+/-0.293) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.318) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.679 (+/-0.294) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.314) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.314) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.317) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.314) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.314) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.314) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.299) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.315) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.312) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.315) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.299) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.315) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.313) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.315) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.299) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.679 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.298) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.678 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.313) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.314) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.315) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.298) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.308) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.299) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.301) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.309) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.313) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.315) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.303) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.309) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.317) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.314) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.314) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.302) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.622 (+/-0.197) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.365) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.154) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.142) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.146) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.154) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.156) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.594 (+/-0.178) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.619 (+/-0.199) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.615 (+/-0.293) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.142) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.146) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.163) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.594 (+/-0.178) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.581 (+/-0.191) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.611 (+/-0.198) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.591 (+/-0.179) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.154) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.142) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.145) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.154) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.585 (+/-0.160) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.186) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.581 (+/-0.191) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.625 (+/-0.291) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.588 (+/-0.179) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.154) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.142) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.145) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.586 (+/-0.154) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.586 (+/-0.161) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.582 (+/-0.190) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.620 (+/-0.291) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.585 (+/-0.178) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.579 (+/-0.147) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.578 (+/-0.143) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.579 (+/-0.145) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.158) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.594 (+/-0.183) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.580 (+/-0.189) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.614 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.579 (+/-0.147) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.579 (+/-0.144) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.145) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.590 (+/-0.158) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.178) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.580 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.576 (+/-0.187) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.604 (+/-0.282) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.583 (+/-0.178) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.580 (+/-0.147) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.582 (+/-0.146) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.146) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.158) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.589 (+/-0.179) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.582 (+/-0.191) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.580 (+/-0.189) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.576 (+/-0.187) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.186) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.603 (+/-0.282) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.179) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.582 (+/-0.147) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.583 (+/-0.146) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.578 (+/-0.146) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.158) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.186) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.582 (+/-0.191) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.187) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.574 (+/-0.186) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.601 (+/-0.283) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.583 (+/-0.178) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.154) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.152) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.147) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.181) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.577 (+/-0.185) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.579 (+/-0.189) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.186) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.574 (+/-0.186) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.598 (+/-0.280) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.583 (+/-0.178) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.154) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.152) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.579 (+/-0.147) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.181) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.186) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.583 (+/-0.195) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.186) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.574 (+/-0.186) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.593 (+/-0.280) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.585 (+/-0.178) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.587 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.151) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.578 (+/-0.147) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.182) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.583 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.577 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.574 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.582 (+/-0.190) for {'C': 100.0, 'kernel': 'linear'}
0.581 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.575 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.697 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.690 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.699 (+/-0.344) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.696 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.297) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.690 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.699 (+/-0.344) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.696 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.303) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.352) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.349) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.365) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.312) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.299) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.351) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.349) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.365) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.313) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.298) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.295) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.299) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.303) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.352) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.349) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.366) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.314) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.297) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.299) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.716 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.342) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.365) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.296) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.296) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.303) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.716 (+/-0.352) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.342) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.312) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.313) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.296) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.296) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.716 (+/-0.352) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.362) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.296) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.297) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.303) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.716 (+/-0.351) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.364) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.312) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.310) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.297) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.297) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.716 (+/-0.351) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.364) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.312) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.310) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.310) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.691 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.699 (+/-0.344) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.04 0.17 0.06 6
1 0.99 0.96 0.98 623
avg / total 0.98 0.95 0.97 629
本轮grid search结果，得到最好的参数选择是： {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.717144344133894
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1096478.19614319, 1116863.24778056, 1137627.28582343,
1158777.35615513, 1180320.63565173, 1202264.43461741,
1224616.19926505, 1247383.51424294, 1270574.10520854,
1294195.84144999, 1318256.73855641]), 'kernel': ['rbf'], 'gamma': array([6.30957344e-06, 6.91830971e-06, 7.58577575e-06, 8.31763771e-06,
9.12010839e-06, 1.00000000e-05, 1.09647820e-05, 1.20226443e-05,
1.31825674e-05, 1.44543977e-05, 1.58489319e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}, 0.717144344133894)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [3.82628758e+05 5.08219768e-21]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1096478.19614319, 1116863.24778056, 1137627.28582343,
1158777.35615513, 1180320.63565173, 1202264.43461741,
1224616.19926505, 1247383.51424294, 1270574.10520854,
1294195.84144999, 1318256.73855641]), 'kernel': ['rbf'], 'gamma': array([6.30957344e-06, 6.91830971e-06, 7.58577575e-06, 8.31763771e-06,
9.12010839e-06, 1.00000000e-05, 1.09647820e-05, 1.20226443e-05,
1.31825674e-05, 1.44543977e-05, 1.58489319e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.590 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.182) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.593 (+/-0.157) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.604 (+/-0.182) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.602 (+/-0.182) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.599 (+/-0.164) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.601 (+/-0.159) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.594 (+/-0.159) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.590 (+/-0.154) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.595 (+/-0.161) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.593 (+/-0.157) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.604 (+/-0.182) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.602 (+/-0.182) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.600 (+/-0.164) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.594 (+/-0.159) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.154) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.595 (+/-0.161) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.593 (+/-0.157) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.602 (+/-0.181) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.602 (+/-0.182) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.600 (+/-0.164) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.154) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.595 (+/-0.161) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.589 (+/-0.152) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.606 (+/-0.183) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.600 (+/-0.164) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.154) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.595 (+/-0.161) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.587 (+/-0.150) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.602 (+/-0.181) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.603 (+/-0.164) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.592 (+/-0.155) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.587 (+/-0.150) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.606 (+/-0.183) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.603 (+/-0.164) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.181) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.592 (+/-0.155) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.591 (+/-0.156) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.607 (+/-0.182) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.603 (+/-0.164) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.604 (+/-0.182) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.591 (+/-0.156) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.607 (+/-0.182) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.601 (+/-0.165) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.603 (+/-0.182) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.595 (+/-0.161) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.593 (+/-0.155) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.607 (+/-0.182) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.603 (+/-0.164) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.603 (+/-0.182) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.591 (+/-0.156) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.597 (+/-0.161) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.607 (+/-0.182) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.162) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.604 (+/-0.164) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.601 (+/-0.163) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.592 (+/-0.156) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.597 (+/-0.161) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.607 (+/-0.182) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.162) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.604 (+/-0.164) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.601 (+/-0.163) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.599 (+/-0.164) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.604 (+/-0.181) for {'C': 10.0, 'kernel': 'linear'}
0.609 (+/-0.201) for {'C': 100.0, 'kernel': 'linear'}
0.584 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.577 (+/-0.192) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.695 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.695 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.695 (+/-0.301) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.303) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.303) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.302) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.302) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.304) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.302) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.302) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.695 (+/-0.300) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.302) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.304) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.300) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.696 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.301) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.300) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.302) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.696 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.695 (+/-0.301) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.301) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.303) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.696 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.301) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.300) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.696 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.299) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.303) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.301) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.694 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.303) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.302) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.581 (+/-0.146) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.584 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.587 (+/-0.159) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.586 (+/-0.161) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.590 (+/-0.163) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.582 (+/-0.163) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.582 (+/-0.145) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.584 (+/-0.154) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.155) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.154) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.587 (+/-0.159) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.590 (+/-0.158) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.582 (+/-0.163) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.146) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.584 (+/-0.155) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.155) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.155) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.587 (+/-0.160) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.590 (+/-0.158) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.582 (+/-0.163) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.584 (+/-0.154) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.155) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.155) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.587 (+/-0.160) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.589 (+/-0.159) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.160) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.584 (+/-0.154) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.155) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.155) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.589 (+/-0.161) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.589 (+/-0.159) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.160) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.154) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.587 (+/-0.154) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.155) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.589 (+/-0.162) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.590 (+/-0.158) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.160) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.580 (+/-0.151) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.587 (+/-0.154) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.585 (+/-0.154) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.587 (+/-0.155) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.155) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.589 (+/-0.162) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.589 (+/-0.159) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.161) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.581 (+/-0.153) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.155) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.585 (+/-0.154) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.586 (+/-0.154) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.155) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.589 (+/-0.162) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.589 (+/-0.159) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.161) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.155) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.155) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.585 (+/-0.154) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.586 (+/-0.154) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.155) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.588 (+/-0.162) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.588 (+/-0.159) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.161) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.155) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.584 (+/-0.155) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.585 (+/-0.154) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.586 (+/-0.154) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.155) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.588 (+/-0.162) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.588 (+/-0.159) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.161) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.581 (+/-0.153) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.583 (+/-0.156) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.577 (+/-0.146) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.585 (+/-0.154) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.586 (+/-0.154) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.155) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.588 (+/-0.162) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.588 (+/-0.159) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.586 (+/-0.162) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.586 (+/-0.161) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.582 (+/-0.190) for {'C': 100.0, 'kernel': 'linear'}
0.581 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.575 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.693 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.693 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.692 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.692 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.693 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.692 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.692 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.690 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.693 (+/-0.304) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.304) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.692 (+/-0.304) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.692 (+/-0.302) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.717 (+/-0.351) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.690 (+/-0.303) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.693 (+/-0.304) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.304) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.692 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.692 (+/-0.302) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.717 (+/-0.351) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.690 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.693 (+/-0.304) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.304) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.692 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.692 (+/-0.301) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.351) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.715 (+/-0.348) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.303) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.693 (+/-0.304) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.692 (+/-0.303) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.692 (+/-0.301) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.351) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.303) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.303) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.352) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.717 (+/-0.351) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.352) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.351) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.303) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.303) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.351) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.351) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.306) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.351) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.303) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.350) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.307) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.303) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.351) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.303) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.350) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.307) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.351) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.303) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.350) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.690 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.04 0.17 0.06 6
1 0.99 0.96 0.98 623
avg / total 0.98 0.95 0.97 629
本轮grid search结果，得到最好的参数选择是： {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.717144344133894
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1180320.63565173, 1184677.11758149, 1189049.6789851 ,
1193438.37921079, 1197843.27782586, 1202264.43461741,
1206701.90959327, 1211155.76298273, 1215626.05523738,
1220112.84703193, 1224616.19926505]), 'kernel': ['rbf'], 'gamma': array([9.12010839e-06, 9.28966387e-06, 9.46237161e-06, 9.63829024e-06,
9.81747943e-06, 1.00000000e-05, 1.01859139e-05, 1.03752842e-05,
1.05681751e-05, 1.07646521e-05, 1.09647820e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}, 0.717144344133894)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [2.79396772e-09 0.00000000e+00]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 9.999999999999996e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 1202264.4346174113, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9447200566973778
测试集中，预测为舞弊样本的有： (array([ 1, 2, 3, 4, 7, 8, 10, 11, 12, 13, 14,
17, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
41, 42, 43, 44, 45, 46, 47, 49, 50, 51, 52,
53, 54, 57, 58, 59, 60, 61, 62, 63, 64, 65,
66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76,
77, 78, 79, 80, 81, 82, 83, 84, 87, 88, 89,
90, 91, 92, 93, 102, 107, 108, 109, 110, 111, 112,
113, 114, 115, 116, 117, 118, 119, 120, 121, 127, 128,
129, 133, 135, 136, 140, 141, 142, 143, 144, 145, 146,
147, 148, 149, 150, 151, 153, 154, 155, 156, 157, 158,
162, 163, 164, 165, 166, 176, 177, 178, 179, 180, 181,
182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192,
193, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,
206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216,
217, 218, 219, 223, 225, 226, 227, 228, 229, 230, 231,
232, 233, 234, 235, 236, 237, 238, 239, 242, 243, 244,
245, 246, 247, 251, 252, 253, 254, 255, 256, 257, 258,
259, 260, 261, 262, 264, 267, 268, 269, 270, 274, 275,
276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,
287, 288, 289, 290, 292, 293, 295, 296, 297, 298, 299,
300, 301, 302, 306, 307, 308, 309, 310, 313, 316, 319,
320, 321, 322, 323, 324, 325, 330, 331, 332, 333, 334,
335, 336, 337, 338, 339, 340, 342, 343, 344, 346, 347,
348, 349, 351, 352, 353, 354, 356, 357, 358, 359, 360,
361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371,
372, 373, 374, 376, 377, 378, 381, 382, 383, 384, 385,
386, 387, 388, 391, 392, 393, 394, 395, 398, 399, 403,
404, 406, 409, 410, 411, 412, 413, 414, 417, 419, 420,
421, 422, 423, 426, 427, 429, 430, 431, 432, 433, 434,
436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,
447, 448, 449, 450, 451, 453, 454, 455, 456, 457, 460,
461, 462, 463, 464, 465, 466, 467, 468, 469, 471, 472,
473, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486,
487, 488, 489, 490, 491, 492, 493, 495, 496, 498, 499,
500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510,
511, 512, 513, 514, 515, 516, 517, 518, 519, 521, 523,
525, 529, 530, 531, 532, 534, 535, 537, 539, 540, 541,
542, 543, 544, 545, 546, 547, 549, 550, 551, 552, 555,
557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567,
568, 570, 571, 572, 573, 578, 582, 583, 584, 586, 587,
589, 590, 591, 592, 593, 594, 595, 599, 600, 601, 602,
603, 604, 608, 610, 611, 612, 613, 614, 615, 616, 617,
618, 619, 620, 621, 622, 623, 624, 627, 628, 629, 630,
631, 633, 634, 635, 641, 642, 643, 645, 646, 649, 651,
652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662,
663, 664, 665, 666, 667, 668, 669, 670, 673, 674, 675,
676, 677, 678, 680, 681, 682, 683, 684, 685, 686, 687,
688, 692, 694, 695, 696, 697, 698, 699, 701, 702, 703,
706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716,
717, 718, 719, 720, 721, 722, 723, 724, 725, 727, 729,
733, 734, 735, 736, 737, 739, 740, 741, 742, 743, 750,
751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761,
767, 768, 769, 770, 773, 774, 775, 776, 777, 778, 784,
785, 786, 787, 788, 789, 790, 792, 793, 794, 795, 796,
797, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812,
813, 814, 815, 816, 817, 818, 819, 820, 822, 823, 824,
825, 826, 827, 828, 829, 830, 831, 835, 839, 840, 841,
843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853,
854, 855, 856, 857, 858, 859, 860, 861, 862, 864, 865,
866, 868, 872, 873, 874, 875, 876, 877, 878, 879, 880,
881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 892,
893, 897, 898, 900, 901, 902, 903, 904, 905, 906, 908,
910, 911, 912, 913, 914, 917, 919, 920, 922, 923, 924,
925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935,
941, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954,
955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965,
966, 967, 970, 971, 972, 976, 977, 978, 979, 980, 981,
982, 983, 984, 985, 992, 993, 994, 995, 998, 999, 1000,
1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011,
1012, 1013, 1014, 1015, 1016, 1017, 1020, 1021, 1022, 1027, 1028,
1030, 1034, 1035, 1036, 1037, 1039, 1040, 1041, 1042, 1046, 1047,
1048, 1049, 1050, 1051, 1055, 1056, 1057, 1058, 1060, 1061, 1064,
1065, 1066, 1067, 1068, 1071, 1073, 1074, 1075, 1076, 1079, 1080,
1081, 1082, 1083, 1084, 1085, 1086, 1089, 1090, 1093, 1094, 1095,
1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106,
1107, 1110, 1111, 1112, 1113, 1114, 1115, 1117, 1118, 1119, 1121,
1122, 1123, 1124, 1125, 1127, 1129, 1131, 1132, 1134, 1135, 1137,
1138, 1139, 1140, 1141, 1142, 1144, 1148, 1151, 1152, 1153, 1154,
1156, 1158, 1160, 1162, 1163, 1164, 1167, 1168, 1169, 1170, 1171,
1172, 1175, 1177, 1180, 1183, 1184, 1186, 1188, 1189, 1191, 1192,
1194, 1196, 1199, 1200, 1204, 1205, 1207, 1208, 1210, 1211, 1214,
1215, 1216, 1217, 1218, 1219, 1220, 1222, 1226, 1227, 1228, 1230,
1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243,
1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1254, 1255,
1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 936
训练模型SVC对测试样本的预测准确率： 0.3330970942593905
以上是第42次特征筛选。
第42次特征筛选，AUC值是： 0.5829563694732233
X_train_iter_svc.shape is: (1257, 10)
X_test_iter_svc.shape is: (1257, 10)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.602 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.577 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.658 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6982356336054085
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6982356336054085
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.690 (+/-0.300) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.604 (+/-0.159) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.601 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.594 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.567 (+/-0.197) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.219) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.157) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.188) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.219) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.594 (+/-0.157) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.589 (+/-0.187) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.565 (+/-0.196) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.765 (+/-0.428) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.595 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.591 (+/-0.191) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.590 (+/-0.193) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.191) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.691 (+/-0.371) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.182) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.587 (+/-0.186) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.199) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.573 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.370) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.181) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.593 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.573 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.599 (+/-0.162) for {'C': 10.0, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 100.0, 'kernel': 'linear'}
0.580 (+/-0.187) for {'C': 1000.0, 'kernel': 'linear'}
0.590 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.696 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.661 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.695 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.584 (+/-0.234) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.686 (+/-0.265) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6991982026547944
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.673 (+/-0.294) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.667 (+/-0.292) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.319) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.667 (+/-0.292) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.199) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.592 (+/-0.295) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.667 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.188) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.154) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.601 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.667 (+/-0.292) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.628 (+/-0.188) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.579 (+/-0.146) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.571 (+/-0.193) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.188) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.146) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.187) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.188) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.579 (+/-0.146) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.187) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.547 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.618 (+/-0.196) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.582 (+/-0.146) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.186) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.193) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.191) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.831 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.538 (+/-0.143) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.570 (+/-0.145) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.185) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.187) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.199) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.573 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.581 (+/-0.163) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.585 (+/-0.193) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.573 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.579 (+/-0.189) for {'C': 100.0, 'kernel': 'linear'}
0.577 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.587 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.294) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.012) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.693 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.610 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.693 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.607 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.311) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.692 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.608 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.525 (+/-0.150) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.305) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.652 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.365) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.575 (+/-0.206) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.366) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.658 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6998392282958199
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.765 (+/-0.428) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.595 (+/-0.154) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.591 (+/-0.191) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.590 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.669 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.596 (+/-0.159) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.584 (+/-0.187) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.589 (+/-0.194) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.735 (+/-0.392) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.630 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.598 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.583 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.193) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.798 (+/-0.438) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.630 (+/-0.189) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.594 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.579 (+/-0.186) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.193) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.726 (+/-0.399) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.630 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.593 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.585 (+/-0.193) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.372) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.587 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.199) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.686 (+/-0.374) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.602 (+/-0.180) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.590 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.815 (+/-0.434) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.677 (+/-0.372) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.181) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.590 (+/-0.187) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.367) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.181) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.594 (+/-0.192) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.646 (+/-0.370) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.181) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.593 (+/-0.192) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.585 (+/-0.193) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.370) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.181) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.593 (+/-0.192) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.192) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.599 (+/-0.162) for {'C': 10.0, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 100.0, 'kernel': 'linear'}
0.580 (+/-0.187) for {'C': 1000.0, 'kernel': 'linear'}
0.590 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.291) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.700 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.682 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.677 (+/-0.244) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.323) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.655 (+/-0.211) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.209) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.664 (+/-0.232) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.321) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.662 (+/-0.231) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.232) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6996789718855635
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.547 (+/-0.301) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.618 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.582 (+/-0.146) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.186) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.666 (+/-0.298) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.154) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.186) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.193) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.582 (+/-0.178) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.584 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.193) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.623 (+/-0.293) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.566 (+/-0.145) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.581 (+/-0.180) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.185) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.193) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.140) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.568 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.585 (+/-0.193) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.547 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.831 (+/-0.449) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.541 (+/-0.143) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.570 (+/-0.145) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.185) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.199) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.815 (+/-0.434) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.534 (+/-0.144) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.569 (+/-0.146) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.581 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.529 (+/-0.146) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.577 (+/-0.158) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.581 (+/-0.187) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.553 (+/-0.296) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.578 (+/-0.156) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.585 (+/-0.193) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.297) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.576 (+/-0.154) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.585 (+/-0.193) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.585 (+/-0.193) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.547 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.831 (+/-0.449) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.297) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.581 (+/-0.163) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.585 (+/-0.193) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.192) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.579 (+/-0.189) for {'C': 100.0, 'kernel': 'linear'}
0.577 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.587 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.525 (+/-0.150) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.297) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.699 (+/-0.345) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.695 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.295) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.706 (+/-0.338) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.276) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.364) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.525 (+/-0.150) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.365) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.323) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.632 (+/-0.212) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.365) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.603 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.302) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.366) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.588 (+/-0.210) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.302) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.367) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.321) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.585 (+/-0.209) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.302) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.366) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.525 (+/-0.150) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.578 (+/-0.209) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.366) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.658 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.07 0.17 0.10 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7060876824140491
循环迭代之前，delta is: [6.01892829e+06 9.99000000e-06]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([2511886.43150958, 2754228.70333817, 3019951.72040201,
3311311.21482591, 3630780.54770102, 3981071.70553498,
4365158.32240166, 4786300.92322638, 5248074.60249773,
5754399.37337156, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([1.00000000e-06, 1.58489319e-06, 2.51188643e-06, 3.98107171e-06,
6.30957344e-06, 1.00000000e-05, 1.58489319e-05, 2.51188643e-05,
3.98107171e-05, 6.30957344e-05, 1.00000000e-04])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.630 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.597 (+/-0.161) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.604 (+/-0.180) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.156) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.185) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.185) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.583 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.298) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.160) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.591 (+/-0.153) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.157) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.169) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.236) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.185) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.584 (+/-0.184) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.583 (+/-0.186) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.299) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.159) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.592 (+/-0.154) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.594 (+/-0.153) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.190) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.236) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.185) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.185) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.582 (+/-0.186) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.295) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.159) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.592 (+/-0.154) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.175) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.191) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.618 (+/-0.236) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.185) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.185) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.186) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.655 (+/-0.295) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.593 (+/-0.159) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.594 (+/-0.154) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.602 (+/-0.175) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.597 (+/-0.193) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.605 (+/-0.198) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.184) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.581 (+/-0.185) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.186) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.644 (+/-0.288) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.592 (+/-0.152) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.179) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.191) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.192) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.184) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.582 (+/-0.185) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.190) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.186) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.289) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.593 (+/-0.152) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.191) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.191) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.599 (+/-0.192) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.184) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.581 (+/-0.185) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.190) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.578 (+/-0.185) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.288) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.589 (+/-0.153) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.153) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.191) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.191) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.599 (+/-0.192) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.583 (+/-0.184) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.582 (+/-0.185) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.186) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.630 (+/-0.288) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.159) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.593 (+/-0.153) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.191) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.191) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.599 (+/-0.192) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.583 (+/-0.184) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.572 (+/-0.161) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.578 (+/-0.185) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.629 (+/-0.288) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.599 (+/-0.175) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.191) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.597 (+/-0.193) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.595 (+/-0.189) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.582 (+/-0.185) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.571 (+/-0.160) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.185) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.627 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.593 (+/-0.155) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.598 (+/-0.175) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.594 (+/-0.190) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.595 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.582 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.571 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.599 (+/-0.162) for {'C': 10.0, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 100.0, 'kernel': 'linear'}
0.580 (+/-0.187) for {'C': 1000.0, 'kernel': 'linear'}
0.590 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.697 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.291) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.664 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.311) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.316) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.312) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.312) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.301) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.290) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.316) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.312) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.313) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.291) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.312) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.317) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.313) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.313) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.290) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.316) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.313) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.312) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.290) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.312) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.312) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.304) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.311) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.312) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.313) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.312) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.303) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.301) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.311) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.313) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.312) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.303) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.312) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.312) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.311) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.303) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.312) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.312) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.310) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.661 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.582 (+/-0.178) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.585 (+/-0.178) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.579 (+/-0.142) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.152) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.156) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.185) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.583 (+/-0.179) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.586 (+/-0.178) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.579 (+/-0.142) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.152) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.149) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.572 (+/-0.159) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.189) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.185) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.576 (+/-0.187) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.186) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.185) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.581 (+/-0.180) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.577 (+/-0.152) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.579 (+/-0.142) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.152) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.146) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.179) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.189) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.578 (+/-0.185) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.576 (+/-0.187) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.187) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.185) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.572 (+/-0.154) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.574 (+/-0.145) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.578 (+/-0.142) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.152) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.170) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.179) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.189) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.575 (+/-0.187) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.576 (+/-0.187) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.187) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.186) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.567 (+/-0.145) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.145) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.578 (+/-0.142) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.152) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.171) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.179) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.189) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.575 (+/-0.187) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.576 (+/-0.187) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.186) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.186) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.566 (+/-0.145) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.144) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.580 (+/-0.144) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.153) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.604 (+/-0.175) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.581 (+/-0.180) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.574 (+/-0.187) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.576 (+/-0.187) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.185) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.567 (+/-0.145) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.144) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.580 (+/-0.144) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.152) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.187) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.577 (+/-0.185) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.574 (+/-0.187) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.575 (+/-0.186) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.185) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.568 (+/-0.146) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.144) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.146) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.152) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.187) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.577 (+/-0.185) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.574 (+/-0.187) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.577 (+/-0.187) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.186) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.568 (+/-0.146) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.572 (+/-0.143) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.588 (+/-0.153) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.152) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.187) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.577 (+/-0.185) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.576 (+/-0.188) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.568 (+/-0.162) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.185) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.568 (+/-0.146) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.144) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.587 (+/-0.153) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.594 (+/-0.176) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.187) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.577 (+/-0.185) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.576 (+/-0.188) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.568 (+/-0.162) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.185) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.568 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.144) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.589 (+/-0.154) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.593 (+/-0.176) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.576 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.568 (+/-0.162) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.579 (+/-0.189) for {'C': 100.0, 'kernel': 'linear'}
0.577 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.587 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.692 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.716 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.699 (+/-0.345) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.363) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.364) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.297) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.741 (+/-0.317) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.367) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.363) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.680 (+/-0.364) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.297) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.724 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.707 (+/-0.340) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.364) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.365) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.296) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.301) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.724 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.707 (+/-0.340) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.364) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.311) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.311) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.295) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.302) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.724 (+/-0.313) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.339) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.364) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.311) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.311) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.295) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.302) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.724 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.338) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.365) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.310) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.296) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.302) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.707 (+/-0.340) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.681 (+/-0.363) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.365) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.310) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.311) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.297) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.304) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.707 (+/-0.340) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.681 (+/-0.363) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.365) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.310) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.298) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.297) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.303) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.304) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.707 (+/-0.340) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.364) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.366) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.310) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.309) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.303) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.707 (+/-0.340) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.364) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.366) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.310) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.309) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.312) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.706 (+/-0.340) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.681 (+/-0.364) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.366) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.658 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.05 0.17 0.07 6
1 0.99 0.97 0.98 623
avg / total 0.98 0.96 0.97 629
本轮grid search结果，得到最好的参数选择是： {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7405417800313299
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([2511886.43150958, 2558585.88690565, 2606153.5499989 ,
2654605.56197554, 2703958.36410884, 2754228.70333816,
2805433.63795172, 2857590.54337495, 2910717.11806661,
2964831.38952434, 3019951.72040201]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-06, 4.36515832e-06, 4.78630092e-06, 5.24807460e-06,
5.75439937e-06, 6.30957344e-06, 6.91830971e-06, 7.58577575e-06,
8.31763771e-06, 9.12010839e-06, 1.00000000e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.7405417800313299)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [1.22684300e+06 3.69042656e-06]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([2511886.43150958, 2558585.88690565, 2606153.5499989 ,
2654605.56197554, 2703958.36410884, 2754228.70333816,
2805433.63795172, 2857590.54337495, 2910717.11806661,
2964831.38952434, 3019951.72040201]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-06, 4.36515832e-06, 4.78630092e-06, 5.24807460e-06,
5.75439937e-06, 6.30957344e-06, 6.91830971e-06, 7.58577575e-06,
8.31763771e-06, 9.12010839e-06, 1.00000000e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.604 (+/-0.180) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.608 (+/-0.180) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.621 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.161) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.156) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.638 (+/-0.289) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.157) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.611 (+/-0.169) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.594 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.598 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.153) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.608 (+/-0.180) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.621 (+/-0.290) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.161) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.159) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.156) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.643 (+/-0.290) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.157) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.604 (+/-0.160) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.602 (+/-0.176) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.598 (+/-0.159) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.154) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.608 (+/-0.180) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.621 (+/-0.290) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.161) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.156) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.643 (+/-0.290) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.157) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.602 (+/-0.176) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.598 (+/-0.159) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.153) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.608 (+/-0.180) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.292) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.161) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.157) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.288) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.156) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.615 (+/-0.184) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.602 (+/-0.176) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.598 (+/-0.159) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.153) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.608 (+/-0.180) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.620 (+/-0.291) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.161) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.157) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.288) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.156) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.602 (+/-0.176) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.585 (+/-0.169) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.153) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.181) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.291) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.601 (+/-0.165) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.157) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.288) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.156) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.610 (+/-0.180) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.601 (+/-0.175) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.585 (+/-0.169) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.153) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.181) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.600 (+/-0.181) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.601 (+/-0.165) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.156) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.288) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.156) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.601 (+/-0.175) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.585 (+/-0.169) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.154) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.211) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.178) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.601 (+/-0.165) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.159) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.288) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.156) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.611 (+/-0.185) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.191) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.154) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.211) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.603 (+/-0.182) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.601 (+/-0.165) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.592 (+/-0.153) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.652 (+/-0.295) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.605 (+/-0.162) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.599 (+/-0.192) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.601 (+/-0.175) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.190) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.154) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.211) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.178) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.600 (+/-0.163) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.592 (+/-0.153) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.652 (+/-0.295) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.160) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.601 (+/-0.175) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.190) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.154) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.211) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.599 (+/-0.178) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.600 (+/-0.163) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.594 (+/-0.153) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.652 (+/-0.295) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.605 (+/-0.163) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.601 (+/-0.175) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.190) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.599 (+/-0.162) for {'C': 10.0, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 100.0, 'kernel': 'linear'}
0.580 (+/-0.187) for {'C': 1000.0, 'kernel': 'linear'}
0.590 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.695 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.696 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.679 (+/-0.291) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.301) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.696 (+/-0.304) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.308) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.305) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.679 (+/-0.294) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.679 (+/-0.291) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.301) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.308) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.305) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.679 (+/-0.291) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.301) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.679 (+/-0.291) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.694 (+/-0.300) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.301) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.679 (+/-0.294) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.694 (+/-0.301) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.307) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.694 (+/-0.301) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.696 (+/-0.306) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.312) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.307) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.302) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.678 (+/-0.288) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.305) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.663 (+/-0.314) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.307) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.694 (+/-0.301) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.678 (+/-0.288) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.307) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.694 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.679 (+/-0.290) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.305) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.587 (+/-0.152) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.582 (+/-0.145) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.579 (+/-0.146) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.585 (+/-0.152) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.588 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.590 (+/-0.156) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.162) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.171) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.582 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.584 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.582 (+/-0.145) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.577 (+/-0.145) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.585 (+/-0.152) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.160) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.590 (+/-0.156) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.599 (+/-0.167) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.584 (+/-0.158) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.584 (+/-0.153) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.582 (+/-0.145) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.577 (+/-0.145) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.585 (+/-0.152) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.589 (+/-0.160) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.590 (+/-0.156) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.599 (+/-0.167) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.584 (+/-0.152) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.582 (+/-0.145) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.581 (+/-0.152) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.585 (+/-0.152) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.588 (+/-0.160) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.157) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.166) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.589 (+/-0.159) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.584 (+/-0.152) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.582 (+/-0.145) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.576 (+/-0.145) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.584 (+/-0.153) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.588 (+/-0.161) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.157) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.166) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.572 (+/-0.159) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.145) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.583 (+/-0.152) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.584 (+/-0.153) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.149) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.166) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.591 (+/-0.181) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.572 (+/-0.159) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.145) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.581 (+/-0.152) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.583 (+/-0.153) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.149) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.166) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.574 (+/-0.153) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.145) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.579 (+/-0.148) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.583 (+/-0.153) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.600 (+/-0.152) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.166) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.589 (+/-0.161) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.179) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.147) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.583 (+/-0.155) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.583 (+/-0.153) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.146) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.186) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.591 (+/-0.165) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.579 (+/-0.189) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.179) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.147) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.579 (+/-0.148) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.584 (+/-0.155) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.146) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.186) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.591 (+/-0.165) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.179) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.147) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.579 (+/-0.148) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.584 (+/-0.155) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.592 (+/-0.146) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.186) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.593 (+/-0.169) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.179) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.579 (+/-0.189) for {'C': 100.0, 'kernel': 'linear'}
0.577 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.587 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.694 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.716 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.691 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.691 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.699 (+/-0.345) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.716 (+/-0.351) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.691 (+/-0.307) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.290) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.699 (+/-0.345) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.716 (+/-0.352) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.691 (+/-0.307) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.699 (+/-0.346) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.716 (+/-0.352) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.691 (+/-0.307) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.699 (+/-0.346) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.305) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.716 (+/-0.352) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.682 (+/-0.367) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.741 (+/-0.317) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.682 (+/-0.367) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.741 (+/-0.317) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.306) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.290) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.707 (+/-0.340) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.741 (+/-0.317) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.707 (+/-0.340) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.304) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.724 (+/-0.314) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.657 (+/-0.310) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.707 (+/-0.340) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.304) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.691 (+/-0.305) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.724 (+/-0.314) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.707 (+/-0.340) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.304) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.691 (+/-0.305) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.724 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.707 (+/-0.340) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.658 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.05 0.17 0.07 6
1 0.99 0.97 0.98 623
avg / total 0.98 0.96 0.97 629
本轮grid search结果，得到最好的参数选择是： {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.74070255173551
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([2805433.63795172, 2815788.29971032, 2826181.17981016,
2836612.41931252, 2847082.15979929, 2857590.54337495,
2868137.71266847, 2878723.81083525, 2889348.98155908,
2900013.36905407, 2910717.11806661]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-06, 5.86138165e-06, 5.97035287e-06, 6.08135001e-06,
6.19441075e-06, 6.30957344e-06, 6.42687717e-06, 6.54636174e-06,
6.66806769e-06, 6.79203633e-06, 6.91830971e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.74070255173551)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [103361.84003678 0. ]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 6.309573444801928e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 2857590.5433749487, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9397590361445783
测试集中，预测为舞弊样本的有： (array([ 8, 10, 11, 12, 13, 14, 17, 19, 20, 21, 22,
24, 25, 30, 31, 32, 34, 35, 36, 37, 38, 39,
40, 41, 42, 43, 44, 45, 46, 47, 51, 52, 53,
54, 57, 58, 59, 60, 61, 62, 64, 65, 66, 67,
71, 72, 73, 74, 75, 78, 79, 80, 88, 90, 91,
92, 93, 107, 108, 109, 110, 111, 112, 113, 114, 115,
116, 117, 118, 119, 120, 121, 123, 124, 126, 127, 128,
129, 135, 136, 141, 142, 143, 144, 145, 146, 147, 148,
149, 150, 151, 154, 155, 156, 157, 158, 162, 163, 165,
166, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185,
186, 187, 190, 191, 192, 193, 195, 196, 197, 198, 199,
200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 212,
213, 214, 215, 216, 225, 230, 231, 232, 233, 235, 236,
237, 238, 239, 242, 243, 244, 245, 246, 247, 251, 252,
253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 264,
267, 268, 269, 270, 275, 276, 277, 278, 280, 281, 283,
284, 285, 286, 287, 288, 289, 295, 296, 298, 299, 300,
301, 302, 306, 307, 309, 313, 316, 319, 320, 322, 323,
324, 325, 330, 331, 332, 333, 334, 337, 338, 339, 340,
347, 348, 349, 351, 353, 354, 356, 357, 358, 359, 360,
361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 372,
383, 384, 385, 386, 387, 388, 391, 392, 395, 399, 403,
404, 410, 411, 413, 414, 419, 420, 421, 422, 423, 429,
430, 431, 432, 433, 434, 436, 438, 439, 440, 441, 442,
443, 444, 446, 447, 448, 449, 450, 454, 455, 456, 457,
460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 471,
472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482,
483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493,
496, 500, 501, 502, 503, 508, 509, 510, 511, 513, 514,
515, 516, 517, 529, 532, 534, 535, 540, 541, 543, 544,
545, 546, 549, 550, 551, 552, 555, 557, 558, 560, 561,
562, 563, 564, 565, 566, 567, 568, 570, 571, 572, 573,
578, 582, 583, 584, 586, 587, 589, 590, 591, 592, 593,
594, 595, 599, 600, 601, 610, 611, 612, 613, 614, 615,
616, 617, 618, 619, 620, 621, 622, 623, 624, 627, 628,
629, 630, 631, 633, 634, 635, 641, 643, 646, 651, 652,
653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663,
664, 665, 666, 667, 668, 669, 674, 675, 676, 677, 684,
686, 687, 688, 692, 694, 695, 696, 697, 698, 699, 701,
702, 703, 706, 707, 708, 709, 710, 711, 712, 713, 714,
715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725,
727, 729, 735, 736, 742, 743, 750, 751, 752, 753, 754,
756, 757, 758, 759, 760, 761, 769, 770, 774, 775, 776,
777, 778, 785, 786, 787, 788, 789, 790, 792, 793, 794,
795, 797, 806, 807, 810, 811, 812, 814, 815, 816, 817,
818, 819, 820, 825, 826, 827, 828, 829, 830, 835, 840,
841, 843, 844, 845, 850, 851, 853, 854, 856, 857, 859,
860, 861, 862, 864, 872, 874, 875, 876, 877, 878, 879,
880, 881, 882, 883, 884, 885, 886, 892, 893, 897, 898,
900, 901, 902, 903, 904, 905, 906, 910, 911, 912, 913,
914, 919, 923, 924, 925, 927, 928, 929, 930, 931, 932,
933, 934, 935, 946, 947, 948, 949, 950, 951, 952, 953,
954, 955, 956, 958, 959, 960, 961, 962, 963, 964, 965,
966, 967, 970, 971, 976, 977, 978, 979, 980, 981, 982,
983, 984, 985, 992, 993, 994, 995, 998, 1002, 1003, 1004,
1005, 1006, 1008, 1009, 1011, 1012, 1013, 1014, 1015, 1016, 1017,
1021, 1022, 1030, 1035, 1036, 1037, 1039, 1040, 1041, 1046, 1047,
1048, 1049, 1050, 1051, 1055, 1056, 1057, 1060, 1061, 1065, 1067,
1068, 1071, 1073, 1074, 1083, 1085, 1086, 1089, 1092, 1093, 1094,
1095, 1096, 1097, 1098, 1100, 1101, 1102, 1103, 1105, 1106, 1107,
1110, 1112, 1113, 1114, 1117, 1118, 1119, 1121, 1122, 1123, 1124,
1125, 1127, 1129, 1131, 1132, 1134, 1135, 1137, 1140, 1141, 1142,
1144, 1148, 1153, 1154, 1156, 1158, 1160, 1164, 1167, 1168, 1171,
1172, 1175, 1180, 1183, 1184, 1191, 1194, 1196, 1200, 1204, 1205,
1207, 1208, 1211, 1215, 1216, 1217, 1218, 1219, 1220, 1222, 1227,
1230, 1231, 1232, 1233, 1234, 1236, 1237, 1238, 1239, 1240, 1241,
1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252,
1254, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 740
训练模型SVC对测试样本的预测准确率： 0.47200566973777464
以上是第43次特征筛选。
第43次特征筛选，AUC值是： 0.6616080548664818
X_train_iter_svc.shape is: (1257, 9)
X_test_iter_svc.shape is: (1257, 9)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.572 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.685 (+/-0.297) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.585 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.565 (+/-0.151) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.653 (+/-0.324) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6983958900156649
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6982356336054085
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.685 (+/-0.297) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.714 (+/-0.474) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.685 (+/-0.297) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.660 (+/-0.390) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.319) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.685 (+/-0.297) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.620 (+/-0.225) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.321) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.572 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.219) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.598 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.219) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.225) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.192) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.690 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.637 (+/-0.210) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.601 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.568 (+/-0.198) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.459) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.599 (+/-0.244) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.300) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.142) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.156) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.790 (+/-0.443) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.380) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.155) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.156) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.574 (+/-0.193) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.211) for {'C': 10.0, 'kernel': 'linear'}
0.584 (+/-0.188) for {'C': 100.0, 'kernel': 'linear'}
0.576 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.599 (+/-0.183) for {'C': 10000.0, 'kernel': 'linear'}
0.602 (+/-0.181) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.234) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.636 (+/-0.231) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.586 (+/-0.233) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.228) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.234) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.226) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.673 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.288) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.592 (+/-0.262) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.217) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.628 (+/-0.272) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.677 (+/-0.284) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.289) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.266) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.619 (+/-0.210) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.687 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.265) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.692 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.691 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6990374309506142
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.291) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.646 (+/-0.204) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.204) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.643 (+/-0.202) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.449) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.646 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.202) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.187) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.596 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.202) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.580 (+/-0.180) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.568 (+/-0.175) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.572 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.213) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.202) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.186) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.560 (+/-0.195) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.189) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.181) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.562 (+/-0.164) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.389) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.600 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.564 (+/-0.140) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.157) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.568 (+/-0.198) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.823 (+/-0.451) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.586 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.637 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.563 (+/-0.144) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.161) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.790 (+/-0.443) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.608 (+/-0.198) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.560 (+/-0.155) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.570 (+/-0.160) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.574 (+/-0.193) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.600 (+/-0.179) for {'C': 10.0, 'kernel': 'linear'}
0.577 (+/-0.179) for {'C': 100.0, 'kernel': 'linear'}
0.565 (+/-0.151) for {'C': 1000.0, 'kernel': 'linear'}
0.575 (+/-0.158) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.157) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.657 (+/-0.214) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.681 (+/-0.293) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.234) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.009) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.627 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.608 (+/-0.243) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.675 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.675 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.633 (+/-0.235) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.582 (+/-0.234) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.228) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.597 (+/-0.234) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.295) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.674 (+/-0.281) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.671 (+/-0.289) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.266) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.605 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.679 (+/-0.292) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.671 (+/-0.281) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.266) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.506 (+/-0.278) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.281) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.291) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.265) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.658 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.291) for {'C': 10.0, 'kernel': 'linear'}
0.675 (+/-0.286) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.324) for {'C': 1000.0, 'kernel': 'linear'}
0.686 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.686 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6990374309506142
发现最优参数C为原先的最大/最小值，直接重新设置超参。
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.685 (+/-0.297) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.219) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.219) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.225) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.690 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.637 (+/-0.210) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.601 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.599 (+/-0.244) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.300) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.142) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.156) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.380) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.155) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.156) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.848 (+/-0.459) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.787 (+/-0.446) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.381) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.156) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.577 (+/-0.157) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.848 (+/-0.459) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.790 (+/-0.443) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.787 (+/-0.446) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.787 (+/-0.446) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.636 (+/-0.382) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.156) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.157) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.848 (+/-0.459) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.790 (+/-0.443) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.787 (+/-0.446) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.787 (+/-0.446) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.787 (+/-0.446) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.381) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.156) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.577 (+/-0.157) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.787 (+/-0.446) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.787 (+/-0.446) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.787 (+/-0.446) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.787 (+/-0.446) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.381) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.156) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.576 (+/-0.157) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.787 (+/-0.446) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.787 (+/-0.446) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.787 (+/-0.446) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.787 (+/-0.446) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.787 (+/-0.446) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.381) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.156) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.157) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.211) for {'C': 10.0, 'kernel': 'linear'}
0.584 (+/-0.188) for {'C': 100.0, 'kernel': 'linear'}
0.576 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.599 (+/-0.183) for {'C': 10000.0, 'kernel': 'linear'}
0.602 (+/-0.181) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.234) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.234) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.673 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.288) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.628 (+/-0.272) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.677 (+/-0.284) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.289) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.619 (+/-0.210) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.687 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.658 (+/-0.217) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.305) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.616 (+/-0.212) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.686 (+/-0.310) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.310) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.658 (+/-0.217) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.306) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.305) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.305) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.615 (+/-0.211) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.686 (+/-0.308) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.311) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.658 (+/-0.217) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.305) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.305) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.305) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.615 (+/-0.212) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.686 (+/-0.310) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.310) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.305) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.305) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.305) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.305) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.615 (+/-0.212) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.686 (+/-0.310) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.308) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.699 (+/-0.305) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.305) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.305) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.305) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.305) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.208) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.686 (+/-0.309) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.311) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.692 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.691 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6990374309506142
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.202) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.213) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.202) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.189) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.181) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.389) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.600 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.564 (+/-0.140) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.157) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.586 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.637 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.563 (+/-0.144) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.161) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.823 (+/-0.451) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.608 (+/-0.198) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.560 (+/-0.155) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.570 (+/-0.160) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.823 (+/-0.451) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.298) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.199) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.160) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.823 (+/-0.451) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.790 (+/-0.443) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.298) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.199) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.160) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.823 (+/-0.451) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.790 (+/-0.443) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.790 (+/-0.443) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.298) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.199) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.160) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.823 (+/-0.451) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.298) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.199) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.160) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.554 (+/-0.296) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.605 (+/-0.200) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.160) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.600 (+/-0.179) for {'C': 10.0, 'kernel': 'linear'}
0.577 (+/-0.179) for {'C': 100.0, 'kernel': 'linear'}
0.565 (+/-0.151) for {'C': 1000.0, 'kernel': 'linear'}
0.575 (+/-0.158) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.157) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.675 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.295) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.674 (+/-0.281) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.671 (+/-0.289) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.605 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.679 (+/-0.292) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.671 (+/-0.281) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.506 (+/-0.278) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.281) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.291) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.683 (+/-0.296) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.499 (+/-0.283) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.289) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.282) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.292) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.683 (+/-0.296) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.306) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.498 (+/-0.283) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.289) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.282) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.292) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.683 (+/-0.296) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.498 (+/-0.284) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.289) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.282) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.292) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.683 (+/-0.296) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.498 (+/-0.284) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.289) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.282) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.292) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.520 (+/-0.313) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.288) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.282) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.292) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.658 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.291) for {'C': 10.0, 'kernel': 'linear'}
0.675 (+/-0.286) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.324) for {'C': 1000.0, 'kernel': 'linear'}
0.686 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.686 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6990374309506142
循环迭代之前，delta is: [0. 0.]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 1e-08, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 100000000.0, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9043231750531537
测试集中，预测为舞弊样本的有： (array([ 370, 1247, 1248], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 3
训练模型SVC对测试样本的预测准确率： 0.9036144578313253
以上是第44次特征筛选。
第44次特征筛选，AUC值是： 0.5905078067999416
X_train_iter_svc.shape is: (1257, 8)
X_test_iter_svc.shape is: (1257, 8)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.586 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.681 (+/-0.293) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.33 0.17 0.22 6
1 0.99 1.00 0.99 623
avg / total 0.99 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6810881977079727
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.573 (+/-0.146) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.33 0.17 0.22 6
1 0.99 1.00 0.99 623
avg / total 0.99 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.670 (+/-0.306) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.33 0.17 0.22 6
1 0.99 1.00 0.99 623
avg / total 0.99 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.697594607964383
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6810881977079727
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.697594607964383
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.198) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.436) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.564 (+/-0.195) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.603 (+/-0.195) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.689 (+/-0.436) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.599 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.153) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.689 (+/-0.436) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.643 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.603 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.621 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.285) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.634 (+/-0.190) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.155) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.624 (+/-0.212) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.184) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.580 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.595 (+/-0.177) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.168) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.779 (+/-0.452) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.182) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.172) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 10.0, 'kernel': 'linear'}
0.593 (+/-0.178) for {'C': 100.0, 'kernel': 'linear'}
0.587 (+/-0.179) for {'C': 1000.0, 'kernel': 'linear'}
0.607 (+/-0.184) for {'C': 10000.0, 'kernel': 'linear'}
0.612 (+/-0.181) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.226) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.233) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.677 (+/-0.288) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.677 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.287) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.566 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.255) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 1.0, 'kernel': 'linear'}
0.639 (+/-0.233) for {'C': 10.0, 'kernel': 'linear'}
0.678 (+/-0.289) for {'C': 100.0, 'kernel': 'linear'}
0.677 (+/-0.290) for {'C': 1000.0, 'kernel': 'linear'}
0.693 (+/-0.310) for {'C': 10000.0, 'kernel': 'linear'}
0.694 (+/-0.310) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.36 0.83 0.50 6
1 1.00 0.99 0.99 623
avg / total 0.99 0.98 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6935866518262017
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.214) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.192) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.172) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.623 (+/-0.183) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.395) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.172) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.167) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.581 (+/-0.180) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.549 (+/-0.155) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.163) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.582 (+/-0.180) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.568 (+/-0.175) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.618 (+/-0.172) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.579 (+/-0.178) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.199) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.174) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.614 (+/-0.169) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.180) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.570 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.177) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.166) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.567 (+/-0.144) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.569 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.798 (+/-0.437) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.530 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.586 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.571 (+/-0.147) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.176) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.775 (+/-0.457) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.550 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.578 (+/-0.149) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.558 (+/-0.144) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.592 (+/-0.189) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.177) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.603 (+/-0.179) for {'C': 10.0, 'kernel': 'linear'}
0.568 (+/-0.153) for {'C': 100.0, 'kernel': 'linear'}
0.573 (+/-0.146) for {'C': 1000.0, 'kernel': 'linear'}
0.579 (+/-0.159) for {'C': 10000.0, 'kernel': 'linear'}
0.585 (+/-0.161) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.640 (+/-0.234) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.234) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.009) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.652 (+/-0.207) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.583 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.292) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.676 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.292) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.676 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.675 (+/-0.290) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.218) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.286) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.290) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.596 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.298) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.564 (+/-0.249) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.581 (+/-0.278) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.667 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.537 (+/-0.183) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.686 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.683 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.658 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.291) for {'C': 10.0, 'kernel': 'linear'}
0.675 (+/-0.285) for {'C': 100.0, 'kernel': 'linear'}
0.670 (+/-0.306) for {'C': 1000.0, 'kernel': 'linear'}
0.685 (+/-0.315) for {'C': 10000.0, 'kernel': 'linear'}
0.687 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.33 0.17 0.22 6
1 0.99 1.00 0.99 623
avg / total 0.99 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.697594607964383
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.198) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.436) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.564 (+/-0.195) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.603 (+/-0.195) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.689 (+/-0.436) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.599 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.153) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.689 (+/-0.436) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.643 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.603 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.621 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.285) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.634 (+/-0.190) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.155) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.624 (+/-0.212) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.184) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.580 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.595 (+/-0.177) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.168) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.779 (+/-0.452) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.182) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.172) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.789 (+/-0.443) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.648 (+/-0.209) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.639 (+/-0.201) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.623 (+/-0.183) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.181) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.226) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.233) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.677 (+/-0.288) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.677 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.287) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.566 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.255) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.658 (+/-0.216) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.657 (+/-0.215) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.656 (+/-0.214) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.656 (+/-0.213) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.639 (+/-0.233) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6977543490807157
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.214) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.192) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.172) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.623 (+/-0.183) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.395) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.172) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.167) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.581 (+/-0.180) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.549 (+/-0.155) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.163) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.582 (+/-0.180) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.568 (+/-0.175) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.618 (+/-0.172) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.579 (+/-0.178) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.199) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.174) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.614 (+/-0.169) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.180) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.570 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.177) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.166) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.567 (+/-0.144) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.569 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.798 (+/-0.437) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.530 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.586 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.571 (+/-0.147) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.176) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.775 (+/-0.457) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.550 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.578 (+/-0.149) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.558 (+/-0.144) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.592 (+/-0.189) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.177) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.764 (+/-0.421) for {'C': 0.1, 'kernel': 'linear'}
0.614 (+/-0.179) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.630 (+/-0.180) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.627 (+/-0.185) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.619 (+/-0.169) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.610 (+/-0.163) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.610 (+/-0.163) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.182) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.603 (+/-0.179) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.640 (+/-0.234) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.234) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.009) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.652 (+/-0.207) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.583 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.292) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.676 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.292) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.676 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.675 (+/-0.290) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.218) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.286) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.290) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.596 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.298) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.564 (+/-0.249) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.581 (+/-0.278) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.667 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.537 (+/-0.183) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.686 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.683 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.656 (+/-0.213) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.697 (+/-0.305) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.681 (+/-0.292) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.680 (+/-0.292) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.680 (+/-0.292) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.680 (+/-0.292) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.679 (+/-0.291) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.697594607964383
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.198) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.436) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.564 (+/-0.195) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.603 (+/-0.195) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.689 (+/-0.436) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.599 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.153) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.689 (+/-0.436) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.643 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.603 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.621 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.285) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.634 (+/-0.190) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.155) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.624 (+/-0.212) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.184) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.580 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.595 (+/-0.177) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.168) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.779 (+/-0.452) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.182) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.172) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.648 (+/-0.209) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.648 (+/-0.209) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.639 (+/-0.202) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.646 (+/-0.204) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.226) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.233) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.677 (+/-0.288) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.677 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.287) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.566 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.255) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.657 (+/-0.215) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.682 (+/-0.294) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.681 (+/-0.294) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.7585775750291837, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.214) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.192) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.172) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.623 (+/-0.183) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.395) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.172) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.167) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.581 (+/-0.180) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.549 (+/-0.155) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.163) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.582 (+/-0.180) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.568 (+/-0.175) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.618 (+/-0.172) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.579 (+/-0.178) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.199) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.174) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.614 (+/-0.169) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.180) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.570 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.177) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.166) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.567 (+/-0.144) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.569 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.798 (+/-0.437) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.530 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.586 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.571 (+/-0.147) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.176) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.775 (+/-0.457) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.550 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.578 (+/-0.149) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.558 (+/-0.144) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.592 (+/-0.189) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.177) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.627 (+/-0.185) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.640 (+/-0.234) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.234) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.009) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.652 (+/-0.207) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.583 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.292) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.676 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.292) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.676 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.675 (+/-0.290) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.218) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.286) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.290) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.596 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.298) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.564 (+/-0.249) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.581 (+/-0.278) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.667 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.537 (+/-0.183) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.686 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.683 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.697 (+/-0.305) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.43651583224016594, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.697594607964383
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([0.69183097, 0.70469307, 0.71779429, 0.73113908, 0.74473197,
0.75857758, 0.77268059, 0.78704579, 0.80167806, 0.81658237,
0.83176377]), 'kernel': ['linear']}], {'C': 0.7585775750291837, 'kernel': 'linear'}, 0.6982356336054085)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.12762023]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([0.69183097, 0.70469307, 0.71779429, 0.73113908, 0.74473197,
0.75857758, 0.77268059, 0.78704579, 0.80167806, 0.81658237,
0.83176377]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.198) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.436) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.564 (+/-0.195) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.603 (+/-0.195) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.689 (+/-0.436) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.599 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.153) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.689 (+/-0.436) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.643 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.603 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.621 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.285) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.634 (+/-0.190) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.155) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.624 (+/-0.212) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.184) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.580 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.595 (+/-0.177) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.168) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.779 (+/-0.452) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.182) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.172) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.646 (+/-0.204) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.646 (+/-0.204) for {'C': 0.7046930689671468, 'kernel': 'linear'}
0.646 (+/-0.204) for {'C': 0.7177942912713616, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.7311390834834173, 'kernel': 'linear'}
0.651 (+/-0.211) for {'C': 0.7447319739059889, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.7726805850957021, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.7870457896950985, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.801678063387679, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.8165823713585922, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.8317637711026709, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.226) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.233) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.677 (+/-0.288) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.677 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.287) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.566 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.255) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.698 (+/-0.306) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7046930689671468, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7177942912713616, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.7311390834834173, 'kernel': 'linear'}
0.682 (+/-0.294) for {'C': 0.7447319739059889, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.7726805850957021, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.7870457896950985, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.801678063387679, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.8165823713585922, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.8317637711026709, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.7585775750291837, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.214) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.192) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.172) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.623 (+/-0.183) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.395) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.172) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.167) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.581 (+/-0.180) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.549 (+/-0.155) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.163) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.582 (+/-0.180) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.568 (+/-0.175) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.618 (+/-0.172) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.579 (+/-0.178) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.199) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.174) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.614 (+/-0.169) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.180) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.570 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.177) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.166) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.567 (+/-0.144) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.569 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.798 (+/-0.437) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.530 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.586 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.571 (+/-0.147) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.176) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.775 (+/-0.457) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.550 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.578 (+/-0.149) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.558 (+/-0.144) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.592 (+/-0.189) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.177) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.630 (+/-0.189) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7046930689671468, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7177942912713616, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7311390834834173, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7447319739059889, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7726805850957021, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7870457896950985, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.801678063387679, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.8165823713585922, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.8317637711026709, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.640 (+/-0.234) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.234) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.009) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.652 (+/-0.207) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.583 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.292) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.676 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.292) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.676 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.675 (+/-0.290) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.218) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.286) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.290) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.596 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.298) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.564 (+/-0.249) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.581 (+/-0.278) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.667 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.537 (+/-0.183) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.686 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.683 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.698 (+/-0.306) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7046930689671468, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7177942912713616, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7311390834834173, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7447319739059889, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7726805850957021, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7870457896950985, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.801678063387679, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.8165823713585922, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.8317637711026709, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 0.6918309709189364, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.697594607964383
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([0.74473197, 0.74748073, 0.75023963, 0.75300871, 0.75578801,
0.75857758, 0.76137743, 0.76418762, 0.76700819, 0.76983916,
0.77268059]), 'kernel': ['linear']}], {'C': 0.7585775750291837, 'kernel': 'linear'}, 0.6982356336054085)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [0.]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.7585775750291837, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.8851522842639594
测试集中，预测为舞弊样本的有： (array([ 10, 12, 22, 24, 38, 56, 65, 67, 112, 114, 115,
117, 121, 136, 143, 144, 145, 146, 180, 181, 182, 183,
208, 214, 216, 236, 243, 247, 255, 281, 286, 287, 288,
300, 330, 349, 364, 366, 370, 403, 404, 433, 442, 449,
457, 460, 468, 471, 472, 473, 474, 475, 476, 477, 481,
482, 483, 484, 485, 487, 488, 489, 490, 509, 516, 535,
543, 544, 545, 549, 562, 563, 565, 566, 567, 587, 590,
611, 629, 630, 635, 641, 661, 667, 687, 695, 696, 697,
718, 720, 721, 722, 723, 736, 742, 743, 750, 752, 775,
776, 777, 778, 787, 790, 820, 845, 860, 862, 874, 879,
880, 882, 883, 884, 885, 897, 898, 902, 929, 930, 931,
960, 961, 963, 964, 966, 967, 998, 1013, 1014, 1015, 1016,
1017, 1037, 1049, 1050, 1051, 1067, 1089, 1112, 1113, 1114, 1122,
1124, 1129, 1141, 1142, 1148, 1154, 1156, 1158, 1160, 1164, 1180,
1183, 1194, 1200, 1222, 1230, 1241, 1242, 1243, 1246, 1247, 1248,
1249, 1250, 1251, 1252, 1254, 1255, 1256], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 172
训练模型SVC对测试样本的预测准确率： 0.8781725888324873
以上是第45次特征筛选。
第45次特征筛选，AUC值是： 0.8895374288632715
X_train_iter_svc.shape is: (1257, 7)
X_test_iter_svc.shape is: (1257, 7)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.581 (+/-0.180) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.526 (+/-0.120) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.614 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.548 (+/-0.198) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.499 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6967933259131008
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.578 (+/-0.145) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6966325542089208
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6967933259131008
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6966325542089208
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.581 (+/-0.180) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.326) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.189) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.186) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.284) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.546 (+/-0.170) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.280) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.526 (+/-0.120) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.651 (+/-0.279) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.282) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.173) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.189) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.655 (+/-0.280) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.283) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.601 (+/-0.180) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.560 (+/-0.173) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.666 (+/-0.275) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.630 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.647 (+/-0.279) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.607 (+/-0.283) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.602 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.722 (+/-0.473) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.589 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.646 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.634 (+/-0.372) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.212) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.249) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.721 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.572 (+/-0.190) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.627 (+/-0.280) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.607 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.356) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.212) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.249) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.280) for {'C': 10.0, 'kernel': 'linear'}
0.645 (+/-0.280) for {'C': 100.0, 'kernel': 'linear'}
0.582 (+/-0.153) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.185) for {'C': 10000.0, 'kernel': 'linear'}
0.641 (+/-0.286) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.003) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.523 (+/-0.146) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.563 (+/-0.206) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.523 (+/-0.146) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.695 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.548 (+/-0.198) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.209) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.522 (+/-0.149) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.301) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.302) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.226) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.680 (+/-0.193) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.647 (+/-0.255) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.562 (+/-0.208) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.520 (+/-0.151) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.616 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.662 (+/-0.228) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.691 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.696 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.620 (+/-0.265) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.558 (+/-0.214) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.706 (+/-0.269) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.633 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.691 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.696 (+/-0.305) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.271) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.557 (+/-0.215) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7059284565916398
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.606 (+/-0.146) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.179) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.621 (+/-0.177) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.580 (+/-0.172) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.179) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.179) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.287) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.546 (+/-0.170) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.282) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.518 (+/-0.087) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.179) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.282) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.603 (+/-0.283) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.565 (+/-0.181) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.625 (+/-0.179) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.179) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.282) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.602 (+/-0.283) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.604 (+/-0.182) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.600 (+/-0.112) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.170) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.603 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.160) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.249) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.689 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.529 (+/-0.145) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.602 (+/-0.281) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.595 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.152) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.205) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.249) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.666 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.527 (+/-0.147) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.596 (+/-0.282) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.594 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.361) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.608 (+/-0.208) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.249) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.633 (+/-0.278) for {'C': 10.0, 'kernel': 'linear'}
0.598 (+/-0.284) for {'C': 100.0, 'kernel': 'linear'}
0.578 (+/-0.145) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.180) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.287) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.655 (+/-0.215) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.307) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.216) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.010) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.696 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.627 (+/-0.223) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.523 (+/-0.146) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.695 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.563 (+/-0.206) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.515 (+/-0.152) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.670 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.546 (+/-0.197) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.693 (+/-0.300) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.581 (+/-0.209) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.513 (+/-0.156) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.694 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.692 (+/-0.299) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.552 (+/-0.224) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.760 (+/-0.153) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.690 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.305) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.269) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.554 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.520 (+/-0.151) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.657 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.576 (+/-0.168) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.694 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.607 (+/-0.290) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.558 (+/-0.214) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.681 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.526 (+/-0.114) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.690 (+/-0.287) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.289) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.607 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.557 (+/-0.215) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.691 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.304) for {'C': 10000.0, 'kernel': 'linear'}
0.695 (+/-0.305) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7602533184928684
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.547 (+/-0.301) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.437) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.326) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.547 (+/-0.301) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.436) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.689 (+/-0.374) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.290) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.597 (+/-0.402) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.689 (+/-0.436) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.374) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.643 (+/-0.209) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.172) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.597 (+/-0.402) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.689 (+/-0.436) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.689 (+/-0.374) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.627 (+/-0.185) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.625 (+/-0.179) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.644 (+/-0.164) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.702 (+/-0.420) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.689 (+/-0.374) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.677 (+/-0.299) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.138) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.656 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.666 (+/-0.275) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.693 (+/-0.354) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.635 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.644 (+/-0.120) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.669 (+/-0.142) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.604 (+/-0.171) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.672 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.630 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.710 (+/-0.209) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.618 (+/-0.172) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.677 (+/-0.150) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.625 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.647 (+/-0.460) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.544 (+/-0.101) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.649 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.612 (+/-0.165) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.708 (+/-0.220) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.630 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.677 (+/-0.150) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.192) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.647 (+/-0.460) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.747 (+/-0.502) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.702 (+/-0.429) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.511 (+/-0.032) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.623 (+/-0.295) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.624 (+/-0.166) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.724 (+/-0.211) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.655 (+/-0.285) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.673 (+/-0.153) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.192) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.722 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.739 (+/-0.451) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.691 (+/-0.370) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.503 (+/-0.010) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.595 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.619 (+/-0.169) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.708 (+/-0.232) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.655 (+/-0.285) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.698 (+/-0.247) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.646 (+/-0.287) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.473) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.714 (+/-0.418) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.689 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.732 (+/-0.391) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.453 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.589 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.609 (+/-0.185) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.714 (+/-0.225) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.659 (+/-0.285) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.694 (+/-0.242) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.646 (+/-0.287) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.280) for {'C': 10.0, 'kernel': 'linear'}
0.645 (+/-0.280) for {'C': 100.0, 'kernel': 'linear'}
0.582 (+/-0.153) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.185) for {'C': 10000.0, 'kernel': 'linear'}
0.641 (+/-0.286) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.525 (+/-0.150) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.616 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.615 (+/-0.237) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.525 (+/-0.150) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.640 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.215) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.550 (+/-0.200) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.616 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.640 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.294) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.550 (+/-0.200) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.616 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.640 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.215) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.307) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.722 (+/-0.277) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.641 (+/-0.235) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.640 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.216) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.730 (+/-0.231) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.640 (+/-0.235) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.680 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.656 (+/-0.216) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.730 (+/-0.230) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.677 (+/-0.283) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.721 (+/-0.278) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.681 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.163) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.680 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.575 (+/-0.229) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.616 (+/-0.239) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.658 (+/-0.229) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.720 (+/-0.276) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.696 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.165) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.575 (+/-0.229) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.616 (+/-0.239) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.659 (+/-0.215) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.597 (+/-0.244) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.694 (+/-0.303) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.720 (+/-0.274) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.772 (+/-0.166) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.229) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.616 (+/-0.239) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.641 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.685 (+/-0.198) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.572 (+/-0.213) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.679 (+/-0.253) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.720 (+/-0.272) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.239) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.640 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.666 (+/-0.193) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.539 (+/-0.179) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.662 (+/-0.228) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.694 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.163) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.295) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.303) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7719535823233572
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.597 (+/-0.402) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.437) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.639 (+/-0.326) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.627 (+/-0.185) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.625 (+/-0.179) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.647 (+/-0.460) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.664 (+/-0.388) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.639 (+/-0.326) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.627 (+/-0.185) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.625 (+/-0.179) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.652 (+/-0.123) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.652 (+/-0.313) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.643 (+/-0.306) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.660 (+/-0.290) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.633 (+/-0.120) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.126) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.764 (+/-0.421) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.600 (+/-0.167) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.627 (+/-0.291) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.620 (+/-0.172) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.706 (+/-0.214) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.715 (+/-0.210) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.631 (+/-0.196) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.565 (+/-0.085) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.603 (+/-0.155) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.699 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.248) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.621 (+/-0.177) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.548 (+/-0.097) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.600 (+/-0.112) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.604 (+/-0.143) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.708 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.188) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.723 (+/-0.208) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.616 (+/-0.170) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.722 (+/-0.473) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.527 (+/-0.071) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.613 (+/-0.206) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.581 (+/-0.166) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.704 (+/-0.224) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.635 (+/-0.190) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.138) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.602 (+/-0.159) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.697 (+/-0.437) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.689 (+/-0.436) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.520 (+/-0.060) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.598 (+/-0.184) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.567 (+/-0.126) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.680 (+/-0.235) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.635 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.138) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.595 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.697 (+/-0.437) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.714 (+/-0.352) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.667 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.560 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.558 (+/-0.153) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.575 (+/-0.159) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.639 (+/-0.136) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.635 (+/-0.190) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.649 (+/-0.142) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.586 (+/-0.154) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.437) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.689 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.681 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.725 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.502 (+/-0.445) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.534 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.573 (+/-0.170) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.632 (+/-0.144) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.176) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.667 (+/-0.253) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.605 (+/-0.279) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.673 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.673 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.720 (+/-0.326) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.499 (+/-0.446) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.529 (+/-0.145) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.571 (+/-0.164) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.628 (+/-0.140) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.176) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.660 (+/-0.258) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.602 (+/-0.281) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.633 (+/-0.278) for {'C': 10.0, 'kernel': 'linear'}
0.598 (+/-0.284) for {'C': 100.0, 'kernel': 'linear'}
0.578 (+/-0.145) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.180) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.287) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.550 (+/-0.200) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.616 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.237) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.215) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.575 (+/-0.230) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.616 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.615 (+/-0.237) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.215) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.307) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.746 (+/-0.233) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.640 (+/-0.235) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.640 (+/-0.235) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.215) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.705 (+/-0.151) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.657 (+/-0.217) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.678 (+/-0.190) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.679 (+/-0.291) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.306) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.772 (+/-0.166) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.167) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.697 (+/-0.305) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.676 (+/-0.188) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.701 (+/-0.255) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.234) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.679 (+/-0.275) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.760 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.737 (+/-0.205) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.167) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.612 (+/-0.271) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.725 (+/-0.244) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.677 (+/-0.253) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.616 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.616 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.581 (+/-0.184) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.682 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.659 (+/-0.233) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.770 (+/-0.161) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.616 (+/-0.238) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.217) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.670 (+/-0.183) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.572 (+/-0.193) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.670 (+/-0.306) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.649 (+/-0.243) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.768 (+/-0.160) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.308) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.234) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.694 (+/-0.299) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.238) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.657 (+/-0.216) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.216) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.726 (+/-0.073) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.545 (+/-0.216) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.611 (+/-0.222) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.645 (+/-0.248) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.767 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.693 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.657 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.656 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.656 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.697 (+/-0.143) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.514 (+/-0.275) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.576 (+/-0.168) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.644 (+/-0.248) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.767 (+/-0.160) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.692 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.691 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.304) for {'C': 10000.0, 'kernel': 'linear'}
0.695 (+/-0.305) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7721138387336136
循环迭代之前，delta is: [1.16415322e-10 5.30957344e-07]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([ 630957.34448019, 691830.97091894, 758577.57502918,
831763.77110267, 912010.83935591, 1000000. ,
1096478.19614319, 1202264.43461741, 1318256.73855641,
1445439.77074593, 1584893.19246111]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-07, 4.36515832e-07, 4.78630092e-07, 5.24807460e-07,
5.75439937e-07, 6.30957344e-07, 6.91830971e-07, 7.58577575e-07,
8.31763771e-07, 9.12010839e-07, 1.00000000e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.656 (+/-0.138) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.126) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.625 (+/-0.179) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.667 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.652 (+/-0.123) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.158) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.655 (+/-0.126) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.124) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.126) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.185) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.667 (+/-0.136) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.652 (+/-0.123) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.665 (+/-0.145) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.638 (+/-0.157) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.124) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.126) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.185) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.663 (+/-0.178) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.652 (+/-0.123) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.625 (+/-0.179) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.638 (+/-0.157) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.644 (+/-0.120) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.651 (+/-0.127) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.185) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.668 (+/-0.185) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.652 (+/-0.123) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.638 (+/-0.157) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.164) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.651 (+/-0.127) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.657 (+/-0.134) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.663 (+/-0.178) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.649 (+/-0.126) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.627 (+/-0.185) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.655 (+/-0.126) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.644 (+/-0.120) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.647 (+/-0.124) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.640 (+/-0.164) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.663 (+/-0.178) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.652 (+/-0.123) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.627 (+/-0.185) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.625 (+/-0.179) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.619 (+/-0.169) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.647 (+/-0.124) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.185) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.673 (+/-0.190) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.647 (+/-0.124) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.198) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.636 (+/-0.159) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.630 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.627 (+/-0.185) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.651 (+/-0.127) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.648 (+/-0.164) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.673 (+/-0.190) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.198) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.630 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.630 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.627 (+/-0.185) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.651 (+/-0.127) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.651 (+/-0.211) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.656 (+/-0.218) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.198) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.641 (+/-0.169) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.639 (+/-0.202) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.184) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.216) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.660 (+/-0.177) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.668 (+/-0.185) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.677 (+/-0.150) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.644 (+/-0.209) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.641 (+/-0.169) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.649 (+/-0.181) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.618 (+/-0.172) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.702 (+/-0.219) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.651 (+/-0.167) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.677 (+/-0.150) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.644 (+/-0.209) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.630 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.657 (+/-0.191) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.280) for {'C': 10.0, 'kernel': 'linear'}
0.645 (+/-0.280) for {'C': 100.0, 'kernel': 'linear'}
0.582 (+/-0.153) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.185) for {'C': 10000.0, 'kernel': 'linear'}
0.641 (+/-0.286) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.730 (+/-0.231) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.722 (+/-0.278) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.730 (+/-0.231) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.730 (+/-0.231) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.278) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.730 (+/-0.230) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.279) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.721 (+/-0.277) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.278) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.747 (+/-0.233) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.730 (+/-0.230) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.277) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.278) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.680 (+/-0.294) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.279) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.277) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.697 (+/-0.308) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.277) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.279) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.697 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.309) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.697 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.278) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.722 (+/-0.277) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.680 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.705 (+/-0.270) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.722 (+/-0.278) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7714728130925881
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.689 (+/-0.248) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.216) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.661 (+/-0.132) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.707 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.202) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.631 (+/-0.155) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.627 (+/-0.185) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.621 (+/-0.177) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.184) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.216) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.656 (+/-0.178) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.703 (+/-0.215) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.723 (+/-0.208) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.202) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.631 (+/-0.156) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.184) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.203) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.621 (+/-0.177) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.619 (+/-0.181) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.702 (+/-0.219) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.664 (+/-0.187) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.718 (+/-0.208) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.647 (+/-0.212) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.631 (+/-0.156) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.184) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.631 (+/-0.199) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.615 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.184) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.706 (+/-0.214) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.660 (+/-0.177) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.718 (+/-0.208) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.634 (+/-0.193) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.609 (+/-0.170) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.184) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.626 (+/-0.197) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.616 (+/-0.170) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.184) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.706 (+/-0.214) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.718 (+/-0.208) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.634 (+/-0.193) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.609 (+/-0.170) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.622 (+/-0.184) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.631 (+/-0.199) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.616 (+/-0.170) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.188) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.703 (+/-0.218) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.693 (+/-0.217) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.723 (+/-0.208) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.608 (+/-0.171) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.622 (+/-0.184) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.631 (+/-0.199) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.616 (+/-0.170) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.188) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.706 (+/-0.214) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.693 (+/-0.217) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.668 (+/-0.146) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.608 (+/-0.171) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.198) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.627 (+/-0.199) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.612 (+/-0.169) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.630 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.714 (+/-0.213) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.693 (+/-0.217) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.660 (+/-0.136) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.188) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.607 (+/-0.172) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.624 (+/-0.197) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.625 (+/-0.200) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.617 (+/-0.181) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.190) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.711 (+/-0.215) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.697 (+/-0.216) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.660 (+/-0.136) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.188) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.604 (+/-0.166) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.623 (+/-0.198) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.624 (+/-0.201) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.617 (+/-0.181) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.190) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.711 (+/-0.215) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.656 (+/-0.138) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.188) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.601 (+/-0.166) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.617 (+/-0.188) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.615 (+/-0.186) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.607 (+/-0.163) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.190) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.714 (+/-0.213) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.651 (+/-0.211) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.693 (+/-0.217) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.656 (+/-0.138) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.601 (+/-0.166) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.617 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.615 (+/-0.186) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.602 (+/-0.159) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.633 (+/-0.278) for {'C': 10.0, 'kernel': 'linear'}
0.598 (+/-0.284) for {'C': 100.0, 'kernel': 'linear'}
0.578 (+/-0.145) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.180) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.287) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.747 (+/-0.234) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.165) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.274) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.307) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.309) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.279) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.720 (+/-0.274) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.307) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.309) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.720 (+/-0.274) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.166) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.303) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.307) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.307) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.306) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.166) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.303) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.305) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.308) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.306) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.166) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.166) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.301) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.165) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.166) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.695 (+/-0.305) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.308) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.165) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.166) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.694 (+/-0.299) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.695 (+/-0.303) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.695 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.165) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.166) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.694 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.695 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.695 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.691 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.304) for {'C': 10000.0, 'kernel': 'linear'}
0.695 (+/-0.305) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7721138387336136
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([630957.34448019, 642687.71731702, 654636.17406727, 666806.76921362,
679203.63261718, 691830.97091894, 704693.06896715, 717794.29127136,
731139.08348342, 744731.97390599, 758577.57502918]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-07, 5.86138165e-07, 5.97035287e-07, 6.08135001e-07,
6.19441075e-07, 6.30957344e-07, 6.42687717e-07, 6.54636174e-07,
6.66806769e-07, 6.79203633e-07, 6.91830971e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}, 0.7721138387336136)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [308169.02908106 0. ]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([630957.34448019, 642687.71731702, 654636.17406727, 666806.76921362,
679203.63261718, 691830.97091894, 704693.06896715, 717794.29127136,
731139.08348342, 744731.97390599, 758577.57502918]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-07, 5.86138165e-07, 5.97035287e-07, 6.08135001e-07,
6.19441075e-07, 6.30957344e-07, 6.42687717e-07, 6.54636174e-07,
6.66806769e-07, 6.79203633e-07, 6.91830971e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.652 (+/-0.123) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.635 (+/-0.153) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.638 (+/-0.157) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.655 (+/-0.126) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.642 (+/-0.158) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.635 (+/-0.153) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.635 (+/-0.153) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.638 (+/-0.157) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.655 (+/-0.126) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.660 (+/-0.136) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.635 (+/-0.153) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.638 (+/-0.157) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.655 (+/-0.126) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.660 (+/-0.136) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.635 (+/-0.153) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.638 (+/-0.157) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.655 (+/-0.126) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.660 (+/-0.136) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.635 (+/-0.153) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.635 (+/-0.153) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.638 (+/-0.157) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.660 (+/-0.136) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.655 (+/-0.126) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.665 (+/-0.145) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.655 (+/-0.126) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.665 (+/-0.145) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.652 (+/-0.123) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.655 (+/-0.126) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.665 (+/-0.145) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.622 (+/-0.174) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.652 (+/-0.123) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.655 (+/-0.126) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.665 (+/-0.145) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.622 (+/-0.174) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.622 (+/-0.174) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.652 (+/-0.123) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.660 (+/-0.136) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.665 (+/-0.145) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.622 (+/-0.174) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.622 (+/-0.174) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.638 (+/-0.157) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.652 (+/-0.123) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.660 (+/-0.136) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.669 (+/-0.142) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.665 (+/-0.145) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.622 (+/-0.174) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.622 (+/-0.174) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.625 (+/-0.179) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.280) for {'C': 10.0, 'kernel': 'linear'}
0.645 (+/-0.280) for {'C': 100.0, 'kernel': 'linear'}
0.582 (+/-0.153) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.185) for {'C': 10000.0, 'kernel': 'linear'}
0.641 (+/-0.286) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.746 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.722 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.722 (+/-0.278) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.721 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.722 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.721 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.722 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.721 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.722 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.721 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.722 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.233) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.746 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.696 (+/-0.307) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.746 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.696 (+/-0.307) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.696 (+/-0.307) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.746 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.234) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.696 (+/-0.307) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.696 (+/-0.307) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.722 (+/-0.277) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.746 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.234) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.234) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.696 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.696 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7471138387336137
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.707 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.702 (+/-0.209) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.707 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.707 (+/-0.211) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.707 (+/-0.211) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.715 (+/-0.210) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.707 (+/-0.211) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.707 (+/-0.211) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.715 (+/-0.210) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.715 (+/-0.210) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.715 (+/-0.210) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.723 (+/-0.208) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.639 (+/-0.167) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.707 (+/-0.211) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.723 (+/-0.208) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.647 (+/-0.212) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.707 (+/-0.211) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.723 (+/-0.208) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.647 (+/-0.212) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.718 (+/-0.208) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.647 (+/-0.167) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.647 (+/-0.212) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.718 (+/-0.208) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.647 (+/-0.167) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.630 (+/-0.189) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.647 (+/-0.212) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.698 (+/-0.213) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.718 (+/-0.208) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.639 (+/-0.201) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.647 (+/-0.212) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.633 (+/-0.278) for {'C': 10.0, 'kernel': 'linear'}
0.598 (+/-0.284) for {'C': 100.0, 'kernel': 'linear'}
0.578 (+/-0.145) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.180) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.287) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.772 (+/-0.165) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.771 (+/-0.165) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.165) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.772 (+/-0.165) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.165) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.772 (+/-0.165) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.165) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.165) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.165) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.691 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.304) for {'C': 10000.0, 'kernel': 'linear'}
0.695 (+/-0.305) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7721138387336136
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([679203.63261718, 681710.52630585, 684226.67276595, 686752.10614882,
689286.86073184, 691830.97091894, 694384.47124098, 696947.39635632,
699519.78105121, 702101.66024031, 704693.06896715]), 'kernel': ['rbf'], 'gamma': array([6.19441075e-07, 6.21727389e-07, 6.24022142e-07, 6.26325365e-07,
6.28637088e-07, 6.30957344e-07, 6.33286164e-07, 6.35623580e-07,
6.37969623e-07, 6.40324324e-07, 6.42687717e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}, 0.7721138387336136)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [0. 0.]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 6.309573444801931e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 691830.9709189365, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9142452161587526
测试集中，预测为舞弊样本的有： (array([ 112, 474, 475, 1230, 1247, 1248, 1252, 1255], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 8
训练模型SVC对测试样本的预测准确率： 0.9227498228206945
以上是第46次特征筛选。
第46次特征筛选，AUC值是： 0.6802130453815847
X_train_iter_svc.shape is: (1257, 6)
X_test_iter_svc.shape is: (1257, 6)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.581 (+/-0.180) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.537 (+/-0.134) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.614 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.570 (+/-0.228) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.498 (+/-0.005) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6967933259131008
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.562 (+/-0.158) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6966325542089208
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6967933259131008
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6966325542089208
粗grid search得到的parameter是：
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.581 (+/-0.180) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.326) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.189) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.186) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.652 (+/-0.284) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.538 (+/-0.171) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.650 (+/-0.281) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.592 (+/-0.299) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.537 (+/-0.134) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.650 (+/-0.281) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.669 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.539 (+/-0.135) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.546 (+/-0.171) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.189) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.655 (+/-0.280) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.644 (+/-0.282) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.564 (+/-0.177) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.530 (+/-0.117) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.537 (+/-0.135) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.666 (+/-0.275) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.630 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.650 (+/-0.281) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.288) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.605 (+/-0.196) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.528 (+/-0.108) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.538 (+/-0.113) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.722 (+/-0.473) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.589 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.633 (+/-0.281) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.615 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.568 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.558 (+/-0.124) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.502 (+/-0.035) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.528 (+/-0.108) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.721 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.572 (+/-0.190) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.629 (+/-0.279) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.609 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.380) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.563 (+/-0.159) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.507 (+/-0.061) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.528 (+/-0.108) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.281) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.282) for {'C': 100.0, 'kernel': 'linear'}
0.619 (+/-0.177) for {'C': 1000.0, 'kernel': 'linear'}
0.598 (+/-0.159) for {'C': 10000.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.003) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.522 (+/-0.147) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.012) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.216) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.536 (+/-0.174) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.008) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.228) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.493 (+/-0.012) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.553 (+/-0.185) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.571 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.696 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.562 (+/-0.203) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.569 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.680 (+/-0.193) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.551 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.224) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.616 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.662 (+/-0.228) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.694 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.292) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.519 (+/-0.145) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.558 (+/-0.209) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.706 (+/-0.269) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.633 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.687 (+/-0.289) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.519 (+/-0.150) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.556 (+/-0.212) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.694 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7059284565916398
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.606 (+/-0.146) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.513 (+/-0.101) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.197) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.179) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.621 (+/-0.177) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.573 (+/-0.200) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.179) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.179) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.538 (+/-0.171) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.623 (+/-0.288) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.586 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.530 (+/-0.113) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.179) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.177) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.604 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.539 (+/-0.135) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.522 (+/-0.105) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.625 (+/-0.179) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.621 (+/-0.177) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.285) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.559 (+/-0.163) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.523 (+/-0.102) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.526 (+/-0.106) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.600 (+/-0.112) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.612 (+/-0.164) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.564 (+/-0.187) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.519 (+/-0.068) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.530 (+/-0.107) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.689 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.529 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.598 (+/-0.281) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.590 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.555 (+/-0.160) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.536 (+/-0.095) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.502 (+/-0.035) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.528 (+/-0.108) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.666 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.526 (+/-0.147) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.592 (+/-0.284) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.587 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.536 (+/-0.096) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.507 (+/-0.061) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.528 (+/-0.108) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.597 (+/-0.290) for {'C': 100.0, 'kernel': 'linear'}
0.562 (+/-0.158) for {'C': 1000.0, 'kernel': 'linear'}
0.558 (+/-0.134) for {'C': 10000.0, 'kernel': 'linear'}
0.544 (+/-0.100) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.655 (+/-0.215) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.523 (+/-0.149) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.498 (+/-0.003) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.307) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.655 (+/-0.216) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.009) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.010) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.696 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.231) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.522 (+/-0.147) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.012) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.670 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.535 (+/-0.175) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.488 (+/-0.024) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.244) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.227) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.493 (+/-0.012) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.226) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.551 (+/-0.187) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.560 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.685 (+/-0.299) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.200) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.566 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.760 (+/-0.153) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.690 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.282) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.281) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.550 (+/-0.183) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.575 (+/-0.224) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.657 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.574 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.291) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.279) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.679 (+/-0.276) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.677 (+/-0.284) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.508 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.226) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.681 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.525 (+/-0.114) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.688 (+/-0.284) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.275) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.679 (+/-0.277) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.675 (+/-0.289) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.506 (+/-0.162) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.548 (+/-0.224) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.688 (+/-0.301) for {'C': 100.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 1000.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 10000.0, 'kernel': 'linear'}
0.684 (+/-0.298) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7602533184928684
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.547 (+/-0.301) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.437) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.326) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.547 (+/-0.301) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.436) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.639 (+/-0.326) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.290) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.597 (+/-0.402) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.689 (+/-0.436) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.639 (+/-0.326) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.643 (+/-0.209) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.172) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.597 (+/-0.402) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.689 (+/-0.436) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.639 (+/-0.326) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.627 (+/-0.185) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.625 (+/-0.179) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.702 (+/-0.420) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.689 (+/-0.374) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.677 (+/-0.299) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.639 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.656 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.666 (+/-0.275) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.685 (+/-0.352) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.635 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.644 (+/-0.120) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.669 (+/-0.142) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.547 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.604 (+/-0.171) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.672 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.630 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.714 (+/-0.213) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.614 (+/-0.167) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.686 (+/-0.156) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.621 (+/-0.177) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.647 (+/-0.460) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.544 (+/-0.101) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.648 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.600 (+/-0.138) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.712 (+/-0.218) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.635 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.686 (+/-0.156) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.188) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.647 (+/-0.460) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.747 (+/-0.502) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.702 (+/-0.429) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.511 (+/-0.031) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.623 (+/-0.295) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.624 (+/-0.166) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.720 (+/-0.217) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.631 (+/-0.186) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.677 (+/-0.150) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.624 (+/-0.186) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.722 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.739 (+/-0.451) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.691 (+/-0.370) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.503 (+/-0.010) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.594 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.619 (+/-0.169) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.713 (+/-0.227) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.659 (+/-0.285) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.702 (+/-0.243) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.636 (+/-0.280) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.473) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.714 (+/-0.418) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.689 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.732 (+/-0.391) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.453 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.589 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.609 (+/-0.185) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.707 (+/-0.234) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.659 (+/-0.285) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.702 (+/-0.243) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.280) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.281) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.282) for {'C': 100.0, 'kernel': 'linear'}
0.619 (+/-0.177) for {'C': 1000.0, 'kernel': 'linear'}
0.598 (+/-0.159) for {'C': 10000.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.525 (+/-0.150) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.616 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.615 (+/-0.237) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.525 (+/-0.150) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.615 (+/-0.237) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.215) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.550 (+/-0.200) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.616 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.237) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.294) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.550 (+/-0.200) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.616 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.615 (+/-0.237) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.215) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.307) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.641 (+/-0.235) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.640 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.216) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.705 (+/-0.270) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.640 (+/-0.235) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.680 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.656 (+/-0.215) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.730 (+/-0.230) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.525 (+/-0.150) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.677 (+/-0.283) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.721 (+/-0.278) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.681 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.163) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.680 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.575 (+/-0.229) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.616 (+/-0.239) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.658 (+/-0.229) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.719 (+/-0.276) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.679 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.772 (+/-0.166) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.575 (+/-0.229) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.616 (+/-0.239) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.659 (+/-0.215) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.597 (+/-0.244) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.694 (+/-0.303) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.720 (+/-0.274) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.772 (+/-0.166) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.722 (+/-0.166) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.306) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.229) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.616 (+/-0.239) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.641 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.685 (+/-0.198) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.571 (+/-0.210) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.678 (+/-0.251) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.720 (+/-0.272) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.166) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.239) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.640 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.666 (+/-0.193) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.539 (+/-0.178) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.662 (+/-0.228) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.694 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.161) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.295) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.303) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.694 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7717933259131009
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.597 (+/-0.402) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.437) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.639 (+/-0.326) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.627 (+/-0.185) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.625 (+/-0.179) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.647 (+/-0.460) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.664 (+/-0.388) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.639 (+/-0.326) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.627 (+/-0.185) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.618 (+/-0.172) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.652 (+/-0.123) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.668 (+/-0.295) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.643 (+/-0.306) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.660 (+/-0.290) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.637 (+/-0.122) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.647 (+/-0.124) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.764 (+/-0.421) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.600 (+/-0.167) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.627 (+/-0.291) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.620 (+/-0.172) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.706 (+/-0.214) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.715 (+/-0.210) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.631 (+/-0.196) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.564 (+/-0.085) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.603 (+/-0.155) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.699 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.686 (+/-0.248) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.621 (+/-0.177) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.548 (+/-0.097) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.600 (+/-0.112) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.594 (+/-0.157) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.215) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.623 (+/-0.184) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.673 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.612 (+/-0.164) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.697 (+/-0.437) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.527 (+/-0.071) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.613 (+/-0.206) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.581 (+/-0.166) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.698 (+/-0.224) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.630 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.668 (+/-0.146) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.609 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.697 (+/-0.437) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.664 (+/-0.388) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.520 (+/-0.060) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.590 (+/-0.178) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.567 (+/-0.126) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.684 (+/-0.232) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.630 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.138) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.594 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.697 (+/-0.437) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.714 (+/-0.352) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.667 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.560 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.556 (+/-0.149) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.575 (+/-0.159) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.639 (+/-0.136) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.176) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.138) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.587 (+/-0.154) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.437) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.689 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.681 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.725 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.502 (+/-0.445) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.534 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.573 (+/-0.170) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.634 (+/-0.142) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.176) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.671 (+/-0.249) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.604 (+/-0.280) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.673 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.673 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.720 (+/-0.326) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.499 (+/-0.446) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.529 (+/-0.146) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.567 (+/-0.156) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.632 (+/-0.145) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.176) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.667 (+/-0.253) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.597 (+/-0.281) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.597 (+/-0.290) for {'C': 100.0, 'kernel': 'linear'}
0.562 (+/-0.158) for {'C': 1000.0, 'kernel': 'linear'}
0.558 (+/-0.134) for {'C': 10000.0, 'kernel': 'linear'}
0.544 (+/-0.100) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.550 (+/-0.200) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.616 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.237) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.215) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.575 (+/-0.230) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.616 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.615 (+/-0.237) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.215) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.672 (+/-0.239) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.746 (+/-0.233) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.665 (+/-0.221) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.640 (+/-0.235) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.215) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.705 (+/-0.151) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.722 (+/-0.165) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.657 (+/-0.217) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.678 (+/-0.190) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.679 (+/-0.291) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.306) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.772 (+/-0.166) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.167) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.697 (+/-0.305) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.675 (+/-0.188) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.701 (+/-0.255) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.679 (+/-0.275) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.760 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.712 (+/-0.251) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.616 (+/-0.237) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.612 (+/-0.269) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.725 (+/-0.243) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.677 (+/-0.252) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.163) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.616 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.616 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.581 (+/-0.185) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.682 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.659 (+/-0.234) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.770 (+/-0.160) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.695 (+/-0.300) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.616 (+/-0.238) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.217) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.670 (+/-0.183) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.572 (+/-0.193) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.669 (+/-0.307) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.649 (+/-0.244) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.768 (+/-0.160) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.694 (+/-0.299) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.238) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.657 (+/-0.216) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.216) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.726 (+/-0.074) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.545 (+/-0.214) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.608 (+/-0.222) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.645 (+/-0.249) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.768 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.693 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.657 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.656 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.656 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.697 (+/-0.143) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.514 (+/-0.274) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.574 (+/-0.167) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.644 (+/-0.248) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.767 (+/-0.159) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.691 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.688 (+/-0.301) for {'C': 100.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 1000.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 10000.0, 'kernel': 'linear'}
0.684 (+/-0.298) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7719535823233572
循环迭代之前，delta is: [6.01892829e+05 5.30957344e-07]
执行tunemodel()函数前，使用的grid search parameter是：
[{'C': array([251188.64315096, 275422.87033382, 301995.1720402 , 331131.12148259,
363078.0547701 , 398107.1705535 , 436515.83224017, 478630.09232264,
524807.46024977, 575439.93733716, 630957.34448019]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-07, 4.36515832e-07, 4.78630092e-07, 5.24807460e-07,
5.75439937e-07, 6.30957344e-07, 6.91830971e-07, 7.58577575e-07,
8.31763771e-07, 9.12010839e-07, 1.00000000e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.639 (+/-0.326) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.739 (+/-0.391) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.727 (+/-0.397) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.702 (+/-0.355) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.702 (+/-0.355) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.643 (+/-0.209) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.169) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.614 (+/-0.166) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.623 (+/-0.184) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.618 (+/-0.172) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.618 (+/-0.172) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.372) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.731 (+/-0.393) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.702 (+/-0.355) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.290) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.627 (+/-0.185) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.198) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.614 (+/-0.166) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.618 (+/-0.172) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.618 (+/-0.172) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.618 (+/-0.172) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.614 (+/-0.167) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.727 (+/-0.397) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.702 (+/-0.355) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.295) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.635 (+/-0.198) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.618 (+/-0.169) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.623 (+/-0.183) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.172) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.625 (+/-0.179) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.618 (+/-0.172) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.618 (+/-0.172) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.622 (+/-0.174) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.354) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.693 (+/-0.354) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.185) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.627 (+/-0.185) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.614 (+/-0.166) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.618 (+/-0.172) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.172) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.618 (+/-0.172) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.614 (+/-0.167) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.622 (+/-0.174) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.284) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.627 (+/-0.185) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.618 (+/-0.169) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.623 (+/-0.183) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.610 (+/-0.163) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.625 (+/-0.179) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.172) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.179) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.627 (+/-0.147) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.622 (+/-0.174) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.627 (+/-0.185) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.618 (+/-0.169) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.614 (+/-0.166) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.618 (+/-0.172) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.618 (+/-0.172) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.625 (+/-0.179) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.152) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.179) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.627 (+/-0.147) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.618 (+/-0.169) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.610 (+/-0.163) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.610 (+/-0.163) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.625 (+/-0.179) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.625 (+/-0.179) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.625 (+/-0.179) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.152) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.179) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.627 (+/-0.147) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.167) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.174) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.610 (+/-0.163) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.618 (+/-0.172) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.625 (+/-0.179) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.625 (+/-0.179) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.638 (+/-0.157) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.647 (+/-0.124) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.641 (+/-0.122) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.167) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.188) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.625 (+/-0.179) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.625 (+/-0.179) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.625 (+/-0.179) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.630 (+/-0.189) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.655 (+/-0.126) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.159) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.168) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.642 (+/-0.158) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.625 (+/-0.179) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.625 (+/-0.179) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.643 (+/-0.167) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.655 (+/-0.126) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.158) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.639 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.126) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.663 (+/-0.138) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.643 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.158) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.642 (+/-0.158) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.281) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.282) for {'C': 100.0, 'kernel': 'linear'}
0.619 (+/-0.177) for {'C': 1000.0, 'kernel': 'linear'}
0.598 (+/-0.159) for {'C': 10000.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.615 (+/-0.237) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.657 (+/-0.217) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.657 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.657 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.657 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.656 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.215) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.655 (+/-0.215) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.680 (+/-0.295) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.680 (+/-0.294) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.294) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.236) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.657 (+/-0.216) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.657 (+/-0.216) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.656 (+/-0.215) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.656 (+/-0.215) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.656 (+/-0.215) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.215) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.294) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.680 (+/-0.294) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.680 (+/-0.294) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.294) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.657 (+/-0.216) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.657 (+/-0.216) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.656 (+/-0.216) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.656 (+/-0.215) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.656 (+/-0.215) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.656 (+/-0.215) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.294) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.307) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.680 (+/-0.294) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.680 (+/-0.295) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.307) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.216) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.216) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.656 (+/-0.215) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.656 (+/-0.215) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.655 (+/-0.215) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.680 (+/-0.294) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.294) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.306) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.680 (+/-0.294) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.680 (+/-0.294) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.215) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.215) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.656 (+/-0.215) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.656 (+/-0.215) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.655 (+/-0.215) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.697 (+/-0.307) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.295) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.306) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.307) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.705 (+/-0.269) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.215) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.215) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.655 (+/-0.215) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.680 (+/-0.294) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.680 (+/-0.294) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.697 (+/-0.307) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.705 (+/-0.269) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.307) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.705 (+/-0.269) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.215) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.215) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.655 (+/-0.215) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.307) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.697 (+/-0.307) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.697 (+/-0.307) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.705 (+/-0.269) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.696 (+/-0.307) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.705 (+/-0.269) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.722 (+/-0.278) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.680 (+/-0.294) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.215) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.680 (+/-0.294) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.307) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.697 (+/-0.307) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.722 (+/-0.277) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.730 (+/-0.231) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.696 (+/-0.307) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.730 (+/-0.230) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.722 (+/-0.278) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.680 (+/-0.294) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.307) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.307) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.233) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.696 (+/-0.307) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.705 (+/-0.269) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.722 (+/-0.277) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.722 (+/-0.277) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.233) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.722 (+/-0.278) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.696 (+/-0.307) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.705 (+/-0.270) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.722 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.722 (+/-0.278) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.278) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.694 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7467928106191771
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.637 (+/-0.122) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.639 (+/-0.154) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.147) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.655 (+/-0.126) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.647 (+/-0.124) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.647 (+/-0.124) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.649 (+/-0.126) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.710 (+/-0.209) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.656 (+/-0.138) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.660 (+/-0.136) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.118) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.652 (+/-0.123) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.647 (+/-0.124) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.652 (+/-0.123) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.697 (+/-0.213) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.647 (+/-0.124) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.649 (+/-0.126) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.136) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.656 (+/-0.138) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.167) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.686 (+/-0.221) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.652 (+/-0.123) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.645 (+/-0.127) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.652 (+/-0.123) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.710 (+/-0.209) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.705 (+/-0.207) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.652 (+/-0.123) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.649 (+/-0.126) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.136) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.656 (+/-0.138) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.167) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.709 (+/-0.212) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.647 (+/-0.124) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.645 (+/-0.127) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.639 (+/-0.154) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.710 (+/-0.209) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.710 (+/-0.209) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.129) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.136) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.643 (+/-0.167) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.167) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.709 (+/-0.212) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.647 (+/-0.124) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.641 (+/-0.122) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.647 (+/-0.167) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.710 (+/-0.209) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.710 (+/-0.209) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.129) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.136) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.643 (+/-0.167) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.706 (+/-0.214) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.698 (+/-0.213) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.654 (+/-0.137) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.177) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.710 (+/-0.209) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.129) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.643 (+/-0.167) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.706 (+/-0.214) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.698 (+/-0.213) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.646 (+/-0.122) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.643 (+/-0.202) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.715 (+/-0.210) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.643 (+/-0.167) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.649 (+/-0.126) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.643 (+/-0.167) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.248) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.698 (+/-0.213) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.658 (+/-0.136) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.715 (+/-0.210) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.644 (+/-0.164) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.643 (+/-0.167) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.248) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.701 (+/-0.212) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.658 (+/-0.136) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.715 (+/-0.210) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.198) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.640 (+/-0.164) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.626 (+/-0.188) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.686 (+/-0.248) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.701 (+/-0.212) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.658 (+/-0.136) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.715 (+/-0.210) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.202) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.640 (+/-0.164) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.626 (+/-0.188) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.686 (+/-0.248) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.216) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.661 (+/-0.132) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.202) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.632 (+/-0.154) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.627 (+/-0.185) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.203) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.621 (+/-0.177) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.597 (+/-0.290) for {'C': 100.0, 'kernel': 'linear'}
0.562 (+/-0.158) for {'C': 1000.0, 'kernel': 'linear'}
0.558 (+/-0.134) for {'C': 10000.0, 'kernel': 'linear'}
0.544 (+/-0.100) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：
0.705 (+/-0.151) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.722 (+/-0.277) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.705 (+/-0.269) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.730 (+/-0.231) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.722 (+/-0.165) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.772 (+/-0.166) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.705 (+/-0.151) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.730 (+/-0.231) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.746 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.755 (+/-0.173) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.722 (+/-0.165) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.747 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.747 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.722 (+/-0.278) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.730 (+/-0.066) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.730 (+/-0.231) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.746 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.166) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.166) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.746 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.747 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.747 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.722 (+/-0.278) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.772 (+/-0.166) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.730 (+/-0.231) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.721 (+/-0.277) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.166) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.747 (+/-0.233) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.722 (+/-0.278) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.722 (+/-0.278) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.772 (+/-0.166) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.730 (+/-0.230) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.277) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.166) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.747 (+/-0.233) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.279) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.772 (+/-0.166) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.165) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.746 (+/-0.233) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.277) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.166) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.279) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.772 (+/-0.166) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.165) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.730 (+/-0.230) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.307) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.167) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.722 (+/-0.277) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.279) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.235) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.165) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.167) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.279) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.235) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.167) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.276) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.746 (+/-0.234) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.167) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.276) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.309) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.275) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.307) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.309) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.688 (+/-0.301) for {'C': 100.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 1000.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 10000.0, 'kernel': 'linear'}
0.684 (+/-0.298) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1 1]
交叉验证集预测标签有： [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果，得到最好的参数选择是： {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7719535823233572
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([363078.0547701 , 369828.17978027, 376703.79898391, 383707.24549228,
390840.8957924 , 398107.1705535 , 405508.53544838, 413047.50199016,
420726.62838444, 428548.52039744, 436515.83224017]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-07, 5.86138165e-07, 5.97035287e-07, 6.08135001e-07,
6.19441075e-07, 6.30957344e-07, 6.42687717e-07, 6.54636174e-07,
6.66806769e-07, 6.79203633e-07, 6.91830971e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}, 0.7719535823233572)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [9.31322575e-10 0.00000000e+00]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 6.309573444801931e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 398107.17055349785, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率： 0.9128277817150957
测试集中，预测为舞弊样本的有： (array([ 112, 1230, 1247, 1248], dtype=int64),)
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目： 4
训练模型SVC对测试样本的预测准确率： 0.9029057406094968
以上是第47次特征筛选。
第47次特征筛选，AUC值是： 0.5901065226907923
X_train_iter_svc.shape is: (1257, 5)
X_test_iter_svc.shape is: (1257, 5)
AUC值随特征数目变化： [0.8173792499635195, 0.8173792499635195, 0.8173792499635195, 0.6814168977090325, 0.6814168977090325, 0.8173792499635195, 0.8173792499635195, 0.8169779658543703, 0.8169779658543703, 0.8169779658543703, 0.726871443163578, 0.726871443163578, 0.726871443163578, 0.726871443163578, 0.726871443163578, 0.726871443163578, 0.8177805340726688, 0.726871443163578, 0.6814168977090325, 0.6814168977090325, 0.6814168977090325, 0.6814168977090325, 0.6818181818181819, 0.6818181818181819, 0.6818181818181819, 0.6818181818181819, 0.7264701590544287, 0.5909090909090908, 0.5454545454545454, 0.7264701590544287, 0.6997300452356633, 0.6467605428279586, 0.8153728294177732, 0.6997300452356633, 0.8177805340726688, 0.5454545454545454, 0.5454545454545454, 0.5454545454545454, 0.5067123887348606, 0.5352035604844594, 0.5267765941923246, 0.4970815701152779, 0.5829563694732233, 0.6616080548664818, 0.5905078067999416, 0.8895374288632715, 0.6802130453815847, 0.5901065226907923]
样本特征数目： [52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5]
各轮特征筛选得到的最优超参是：
[{'C': 0.6309573444801931, 'kernel': 'linear'}, {'C': 0.6309573444801931, 'kernel': 'linear'}, {'C': 0.6309573444801931, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.6309573444801931, 'kernel': 'linear'}, {'C': 0.6309573444801931, 'kernel': 'linear'}, {'C': 0.5306400191947748, 'kernel': 'linear'}, {'C': 0.5306400191947748, 'kernel': 'linear'}, {'C': 0.5365373995198518, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}, {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}, {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}, {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}, {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}, {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}, {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}, {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}, {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}, {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}, {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}, {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}, {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}, {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}, {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}, {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}, {'C': 0.7585775750291837, 'kernel': 'linear'}, {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}, {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}]
In [865]:
# 根据上一步试验，当{'C': 9.999999999999996, 'gamma': 0.09999999999999998, 'kernel': 'rbf'}, class_weight={-1:10}时，选24个特征时模型最好。
# 通过调节class_weight，我们发现当将class_weight调大时，最优分类超平面将远离负例样本，也就是说更多负例样本将被正确地分类。由此看，似乎
# class_weight就应该按照正负样本的比例设置权重，使其：权重_正例*正例样本数量 = 权重_负例*负例样本数量，或者使权重_负例*负例样本数量
# 稍微大一点也无妨。那么不同kernel之间，这个class_weight是否应有所不同呢？按照前面的grid_serach试验看，似乎应有所不同。因为kernel是
# 将样本从低维映射到高维，目的是在高维依然是线性可分，问题至此变成了2个不同维度的线性SVM，对不均衡样本，class_weight是否应有所不同？
# 以2维和3维简单分析，认为class_weight应有所不同，假设样本在2维是线性不可分的，在3维线性可分，在这两个维度空间中，正负样本的数目比例
# 是不变的，但几何间隔并不一样，甚至核函数映射后，在高维空间是否能够完全线性可分也未必，现实中总存在soft margin的情况，因此利用
# class_weight，使最优分类超平面距离负样本究竟多远，就不一样。也即，class_weight应不同。
class_weight={-1:10}
auc_svc = np.array(auc_values_svc)
best_param_op_idx = np.max(np.where(auc_svc==np.max(auc_svc))[0])
print("最佳超参的索引best_param_op_idx是：", best_param_op_idx)
param_op_svc = best_params_svc[best_param_op_idx]
clf_op_svc = SVC(class_weight=class_weight, random_state=0) # 或者'balanced'
feature_count_op_svc = feature_count_svc[best_param_op_idx]
X_train_input = X_train[:,:feature_count_op_svc]
y_train_input = y_train
X_test_input = X_test[:,:feature_count_op_svc]
y_test_input = y_test
try:
clf_op_svc = svc_op(X_train_input, y_train_input, class_weight,
kernel = param_op_svc['kernel'],
C=param_op_svc['C'],
gamma=param_op_svc['gamma'])
except KeyError:
clf_op_svc = svc_op(X_train_input, y_train_input, class_weight,
kernel = param_op_svc['kernel'],
C = param_op_svc['C'])
y_pred_op_svc = clf_op_svc.predict(X_test_input)
test_sample_weight = np.ones(X_test.shape[0], dtype=int)
test_sample_weight[test_set["label"]==-1]=class_weight[-1]
auc_value_svc = auc(y_test, y_pred_op_svc)
print("对测试集预测结果：\n", y_pred_op_svc)
print("测试集中预测为舞弊样本的index是：\n", np.where(y_pred_op_svc==-1))
print("auc value is:", auc_value_svc)
# 检查支持向量有哪些
print("训练样本中，支持向量是：", clf_op_svc.support_vectors_)
print("训练样本中，支持向量下标是：", clf_op_svc.support_)
print("训练样本中，标签为-1的样本下标是：", np.where(y_train==-1))
print("测试样本中，标签为-1的样本下标是：", np.where(y_test==-1))
print("训练样本中，支持向量个数：", clf_op_svc.support_.size)
print("训练样本数目：", y_train_input.shape)
distance = clf_op_svc.decision_function(X_test_input)
print("根据desicion function，测试样本到最优分类超平面的距离是:\n", distance)
print("根据desicion function，测试样本到最优分类超平面的距离的shape是:\n", distance.shape)
# 测试集中实际舞弊样本的idx是：
Nidx = np.where(y_test==-1)
print("根据desicion function，测试集中真实舞弊样本到最优分类超平面的距离是:\n", distance[Nidx])
# 打印分类结果评估报告
print("得到的分类结果评估报告如下：\n", classification_report(y_test, y_pred_op_svc))
最佳超参的索引best_param_op_idx是： 45
对测试集预测结果：
[ 1 1 1 ... 1 -1 1]
测试集中预测为舞弊样本的index是：
(array([ 370, 1246, 1247, 1248, 1252, 1255], dtype=int64),)
auc value is: 0.726871443163578
训练样本中，支持向量是： [[3.00000000e-01 5.09536366e-02 3.18333487e-02 4.63639400e-01
3.90075291e-02 9.18758298e-01 1.29804903e-01]
[1.00000000e-01 4.73966837e-02 6.00575013e-01 4.48857231e-01
1.92896149e-03 9.20066454e-01 9.50402118e-01]
[1.00000000e-01 4.26945469e-02 1.00000000e+00 4.47333690e-01
2.63705446e-03 9.19612610e-01 1.00000000e+00]
[1.00000000e-01 5.24787432e-02 2.42621560e-02 4.67360952e-01
5.40719382e-01 9.17890871e-01 4.09751391e-02]
[1.00000000e-01 2.00099829e-01 1.08397168e-02 4.38234862e-01
4.53281737e-02 9.05332085e-01 2.41172819e-02]
[1.00000000e-01 1.69757042e-01 9.01655140e-02 4.40798917e-01
7.75189627e-03 9.03510305e-01 7.06157430e-02]
[3.00000000e-01 2.30162535e-02 1.15321383e-02 4.48583622e-01
4.76058010e-02 7.79720746e-01 5.75888869e-02]
[0.00000000e+00 2.11800484e-02 1.11203412e-02 4.69903748e-01
3.52041659e-01 8.13106021e-01 2.36165693e-02]
[1.00000000e-01 1.73176593e-02 6.06008215e-03 4.63031072e-01
2.07417973e-01 8.81985359e-01 2.06227587e-02]
[1.00000000e-01 3.51378378e-02 4.95555223e-01 4.51758984e-01
7.08240943e-01 8.99691673e-01 1.01806111e-01]
[1.00000000e-01 6.78342950e-02 1.32389211e-01 4.57329245e-01
4.42289077e-03 9.46416845e-01 2.35613510e-01]
[3.00000000e-01 5.16093136e-02 7.19590419e-02 4.55863938e-01
6.70428455e-04 9.30792479e-01 2.87978392e-01]
[3.00000000e-01 5.29447578e-02 8.02588859e-02 4.56653850e-01
4.53050369e-04 9.34386629e-01 3.04407936e-01]
[3.00000000e-01 4.68553739e-02 1.49729716e-01 4.54257514e-01
5.48287798e-04 9.35097285e-01 2.82127455e-01]
[2.00000000e-01 5.65174309e-02 1.22197277e-01 4.48612586e-01
2.25233829e-03 9.27910815e-01 2.66856950e-01]
[2.00000000e-01 5.62219472e-02 1.10015402e-01 4.50413022e-01
1.99568149e-03 9.25394544e-01 2.68431701e-01]
[1.00000000e-01 5.33864712e-02 5.51593445e-02 4.51955769e-01
2.95718136e-01 9.30266350e-01 1.19058109e-01]
[1.00000000e-01 4.52076003e-02 8.71596335e-02 4.48391254e-01
3.91732530e-02 9.20656634e-01 2.04225057e-01]
[1.00000000e-01 4.34001277e-02 6.79235956e-01 4.53290845e-01
1.40916959e-01 9.28753232e-01 3.47548345e-02]
[1.00000000e-01 4.86626707e-02 1.28564865e-01 4.32164114e-01
2.30114075e-02 9.32996428e-01 2.50895121e-01]
[1.00000000e-01 4.52092884e-02 2.61834832e-01 4.46119911e-01
1.23335161e-02 9.26031084e-01 2.32891466e-01]
[1.00000000e-01 4.43679582e-02 2.50944811e-01 4.47570119e-01
1.78330741e-02 9.28018786e-01 1.98066367e-01]
[1.00000000e-01 4.44600638e-02 2.17967853e-01 4.54066378e-01
1.94687904e-02 9.23346756e-01 1.84989429e-01]
[1.00000000e-01 1.17997947e-01 1.85536817e-03 4.61214716e-01
4.65230536e-01 9.22535145e-01 6.50189532e-03]
[1.00000000e-01 9.57486077e-02 9.99800766e-02 4.52742908e-01
5.31306480e-02 9.23144540e-01 2.29283111e-01]
[1.00000000e-01 4.71105548e-02 8.78283789e-02 4.47678577e-01
7.15087815e-03 9.21749458e-01 3.10371958e-01]
[1.00000000e-01 4.91626227e-02 8.36521419e-02 4.47634927e-01
6.59990003e-03 9.32117715e-01 2.34287460e-01]
[0.00000000e+00 6.15588710e-02 8.17456196e-02 4.49895281e-01
1.68478778e-02 9.25242652e-01 2.18658906e-01]
[1.00000000e-01 5.57015273e-02 1.07808933e-01 4.51824408e-01
1.86665361e-02 9.26321446e-01 2.47927473e-01]
[1.00000000e-01 5.40952874e-02 1.07975411e-01 4.51765352e-01
9.24373410e-02 9.28070636e-01 1.39124307e-01]
[1.00000000e-01 5.78056793e-02 4.93351288e-02 4.29982536e-01
2.73513825e-01 9.18724138e-01 7.62532177e-02]
[1.00000000e-01 4.78266157e-02 3.28024883e-01 4.47644376e-01
3.28219388e-03 9.17332411e-01 4.68203096e-01]
[1.00000000e-01 6.10288854e-02 1.66623820e-02 4.72198919e-01
5.34327713e-01 9.16885582e-01 3.35211531e-02]
[1.00000000e-01 5.22336908e-02 2.44531794e-01 4.49535091e-01
1.61744900e-01 9.30516452e-01 2.11066741e-01]
[1.00000000e-01 6.73208385e-02 1.55520401e-01 4.55663867e-01
1.49859054e-01 9.50216567e-01 1.55669072e-01]
[1.00000000e-01 6.76143879e-02 1.73620688e-01 4.60964627e-01
1.84058438e-01 9.48014754e-01 1.25993977e-01]
[1.00000000e-01 6.43693776e-02 2.58116492e-01 4.50688582e-01
2.05221594e-01 9.43791383e-01 1.24665364e-01]
[1.00000000e-01 6.01872386e-02 1.31135048e-01 4.54674500e-01
2.71396003e-01 9.39971531e-01 1.02964616e-01]
[3.00000000e-01 5.95128521e-02 2.88183556e-01 4.54675322e-01
9.80009287e-02 9.24764103e-01 2.17611457e-01]
[3.00000000e-01 5.97390895e-02 2.36391555e-01 4.51276061e-01
1.29533665e-01 9.20193030e-01 1.38568067e-01]
[3.00000000e-01 5.45614778e-02 4.04007221e-01 4.50937336e-01
9.83216152e-02 9.28453719e-01 2.22011726e-01]
[3.00000000e-01 5.37880223e-02 6.73946570e-01 4.50418157e-01
6.64999384e-02 9.39765959e-01 1.68022225e-01]
[3.00000000e-01 5.62890834e-02 4.40832753e-01 4.57625860e-01
1.10909636e-01 9.38684420e-01 1.48406764e-01]
[4.00000000e-01 5.48627291e-02 3.12156232e-03 4.82552302e-01
6.39298192e-01 9.23598384e-01 1.58514132e-02]
[2.00000000e-01 6.41912855e-02 6.03849337e-02 4.53138429e-01
5.69648961e-03 9.15212643e-01 2.34119808e-01]
[1.00000000e-01 1.48817896e-01 1.48088223e-01 4.49607910e-01
4.37236651e-02 9.36783644e-01 1.63591415e-01]
[1.00000000e-01 4.10036922e-02 2.94683480e-02 4.50520863e-01
2.25725620e-01 9.18952280e-01 8.20664597e-02]
[5.00000000e-01 5.06881514e-02 1.41004835e-01 4.46307246e-01
1.45095568e-01 9.16047435e-01 2.07858294e-01]
[3.00000000e-01 5.18027742e-02 1.29273821e-01 4.52813570e-01
1.42642532e-01 9.20930526e-01 1.72229428e-01]
[3.00000000e-01 4.55900200e-02 1.00410749e-01 4.49648170e-01
1.44445048e-01 9.20510233e-01 1.67518095e-01]
[1.00000000e-01 5.67087462e-02 1.07372655e-01 4.47220816e-01
3.95821821e-02 9.25778237e-01 2.13215245e-01]
[1.00000000e-01 5.49881392e-02 1.24211296e-01 4.60346029e-01
4.43618097e-02 9.27764109e-01 2.30262004e-01]
[1.00000000e-01 4.91625172e-02 1.71412519e-01 4.43372319e-01
4.26066431e-02 9.23097264e-01 2.72414656e-01]
[1.00000000e-01 4.47937754e-02 2.07525859e-01 4.52166933e-01
7.16040189e-02 9.18224848e-01 2.26137987e-01]
[3.00000000e-01 8.79819792e-02 6.22776797e-03 4.44263910e-01
1.00000000e+00 9.15974540e-01 2.27038821e-02]
[1.00000000e-01 4.11253041e-02 1.76034770e-02 4.49588601e-01
3.29338025e-01 9.18854069e-01 2.55375159e-02]
[1.00000000e-01 5.65530915e-02 4.97558566e-02 4.62829255e-01
2.54548125e-01 9.29462668e-01 8.17928778e-02]
[1.00000000e-01 5.45751583e-02 5.17950575e-02 4.68413176e-01
2.30285718e-01 9.26592289e-01 8.29501015e-02]
[1.00000000e-01 5.09700953e-02 1.76327844e-01 4.47726233e-01
3.06298638e-02 9.24818089e-01 2.34906810e-01]
[1.00000000e-01 5.16424421e-02 1.58764294e-01 4.48581055e-01
6.68211629e-02 9.23485533e-01 2.03678641e-01]
[1.00000000e-01 4.97174723e-02 1.53592677e-01 4.49729924e-01
7.93327784e-02 9.23106719e-01 1.82722181e-01]
[3.00000000e-01 8.43070369e-02 1.50552252e-02 4.28597853e-01
4.76913532e-01 9.24942225e-01 3.43379478e-02]
[3.00000000e-01 6.56127513e-02 1.19166512e-02 4.67230927e-01
6.13385002e-01 9.15488366e-01 2.32358825e-02]
[1.00000000e-01 6.54536501e-02 1.04695258e-01 4.56396161e-01
6.24154902e-04 9.34822478e-01 2.26220211e-01]
[1.00000000e-01 1.07084850e-01 6.16901904e-02 4.49530675e-01
3.62617318e-01 9.38427913e-01 6.27275711e-02]
[1.00000000e-01 1.08129382e-01 4.68654941e-02 4.49363161e-01
4.10532506e-01 9.36298080e-01 6.96173078e-02]
[2.00000000e-01 1.14937605e-01 3.37979281e-02 4.53959563e-01
4.63928420e-01 9.30957790e-01 5.87996097e-02]
[1.00000000e-01 4.37961223e-02 1.07299256e-01 4.52325716e-01
7.76663924e-02 9.21368510e-01 1.26487108e-01]
[1.00000000e-01 4.09228756e-02 1.36686184e-01 4.50376253e-01
6.41432156e-02 9.19529954e-01 1.56953797e-01]
[1.00000000e-01 4.02397322e-02 1.13394360e-01 4.50186247e-01
7.96706830e-02 9.21122983e-01 1.12835776e-01]
[1.00000000e-01 5.97125023e-02 1.84457229e-01 4.48958294e-01
1.48647333e-01 9.42911146e-01 1.97001619e-01]
[1.00000000e-01 7.09419734e-02 1.22598443e-01 4.55242875e-01
1.19371239e-01 9.35929332e-01 1.61258387e-01]
[1.00000000e-01 6.76003206e-02 1.32447790e-01 4.55864144e-01
1.06649779e-01 9.31832233e-01 1.69807021e-01]
[1.00000000e-01 6.05556613e-02 1.75231453e-02 4.50967224e-01
3.46207963e-01 9.20028939e-01 2.47954703e-02]
[1.00000000e-01 4.68022698e-02 1.32688874e-01 4.52496208e-01
8.32493504e-02 9.25163962e-01 2.53004329e-01]
[1.00000000e-01 7.12537048e-02 8.92032625e-02 4.50288542e-01
1.04441562e-01 9.30292580e-01 1.53949521e-01]
[1.00000000e-01 6.32397379e-02 9.10654591e-02 4.50323565e-01
1.23480438e-01 9.30169359e-01 1.60863605e-01]
[1.00000000e-01 1.82231294e-01 1.49640096e-02 4.58769902e-01
4.85439704e-01 9.19676966e-01 2.67355313e-02]
[1.00000000e-01 2.07035291e-01 1.04721260e-02 4.53205496e-01
4.52572029e-01 9.19368913e-01 2.83792653e-02]
[1.00000000e-01 2.43439537e-01 8.82049452e-03 4.60438156e-01
3.68667316e-01 9.03385254e-01 2.38097425e-02]
[3.00000000e-01 4.37228316e-02 1.89981468e-01 4.44482571e-01
1.43443710e-01 9.21659787e-01 1.51496147e-01]
[1.00000000e-01 8.73975883e-02 1.38339232e-02 4.54580216e-01
4.17605365e-01 9.24428601e-01 3.28169092e-02]
[1.00000000e-01 1.13571811e-01 8.57893272e-03 4.53166262e-01
5.79541816e-01 9.20267146e-01 2.60480526e-02]
[1.00000000e-01 4.96960196e-02 1.06115227e-01 4.47209724e-01
2.81434674e-01 9.21611597e-01 9.04172220e-02]
[1.00000000e-01 4.81344786e-02 7.65142904e-02 4.55348560e-01
3.64104529e-01 9.20355901e-01 8.02272033e-02]
[1.00000000e-01 6.56001611e-02 3.97854254e-01 4.53568562e-01
2.20654900e-02 9.17541948e-01 3.83967416e-01]
[1.00000000e-01 1.39799489e-01 2.67991512e-02 4.34473562e-01
6.86381532e-01 9.29935727e-01 3.92805757e-02]
[1.00000000e-01 6.48984137e-02 3.13375819e-02 4.57662834e-01
6.56273053e-01 9.29790851e-01 4.23735890e-02]
[1.00000000e-01 6.29743934e-02 2.52425044e-01 4.52033107e-01
8.30755556e-02 9.27443551e-01 2.80885551e-01]
[1.00000000e-01 1.15871463e-01 9.46019645e-02 4.55143353e-01
1.23702659e-01 9.33021133e-01 1.89034961e-01]
[1.00000000e-01 5.91524478e-02 1.80408139e-02 4.85040869e-01
4.61299975e-01 9.21830589e-01 3.88597379e-02]
[1.00000000e-01 2.21457582e-01 1.10805928e-02 4.65821697e-01
4.32375238e-01 9.15950749e-01 3.70093759e-02]
[1.00000000e-01 7.13498197e-02 2.26781549e-01 4.50482965e-01
2.06641546e-01 9.35172926e-01 1.50910648e-01]
[1.00000000e-01 7.38215855e-02 4.00299750e-02 4.54199485e-01
3.60495192e-01 9.42735769e-01 5.72735522e-02]
[1.00000000e-01 5.67583334e-02 1.49017182e-02 4.59647012e-01
4.31766687e-01 9.30991646e-01 3.66042355e-02]
[1.00000000e-01 1.05576729e-01 3.51246125e-01 4.56298898e-01
5.15826362e-02 9.25896883e-01 2.04470768e-01]
[1.00000000e-01 8.04610362e-02 8.65692220e-02 4.42415921e-01
1.91560672e-01 9.27511566e-01 9.89963964e-02]
[1.00000000e-01 8.09132648e-02 2.31892962e-01 4.64039132e-01
1.92610436e-01 9.47292813e-01 1.10458219e-01]
[1.00000000e-01 5.30850792e-02 5.33522082e-02 4.51999522e-01
5.63384814e-01 9.21467026e-01 6.51879931e-02]
[1.00000000e-01 8.62356994e-02 9.73123111e-02 4.55459174e-01
2.49059867e-01 9.26502618e-01 7.74385258e-02]
[1.00000000e-01 1.39029796e-01 3.28418640e-02 4.59421367e-01
4.41562690e-01 9.26377872e-01 5.22072679e-02]
[1.00000000e-01 6.26818639e-02 1.73876876e-01 4.45146052e-01
1.44111448e-01 9.27706158e-01 1.52159172e-01]
[1.00000000e-01 4.01735455e-02 6.47958444e-01 4.17567535e-01
1.51626596e-02 9.32884797e-01 4.61219389e-02]]
训练样本中，支持向量下标是： [1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 26 48 49 50
51 52 67 68 112 142 144 145 146 192 209 244 245 254
255 286 349 371 404 405 458 459 460 461 472 473 474 475
476 563 604 618 644 694 696 697 720 721 722 723 737 743
752 753 777 778 779 845 846 852 861 862 863 882 883 884
903 904 905 930 957 959 960 963 964 965 1014 1016 1017 1050
1051 1114 1122 1124 1142 1143 1159 1161 1165 1172 1173 1177 1184 1187
1199 1205 1208 1212 1230]
训练样本中，标签为-1的样本下标是： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
测试样本中，标签为-1的样本下标是： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
训练样本中，支持向量个数： 103
训练样本数目： (1257,)
根据desicion function，测试样本到最优分类超平面的距离是:
[ 1.05155175 1.00452876 1.02674393 ... 0.4619497 -1.51103659
0.10535483]
根据desicion function，测试样本到最优分类超平面的距离的shape是:
(1257,)
根据desicion function，测试集中真实舞弊样本到最优分类超平面的距离是:
[-0.61381692 -0.90587131 -1.55419457 0.50349293 0.37889801 0.16567334
-0.28684544 1.34096502 0.4619497 -1.51103659 0.10535483]
得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.83 0.45 0.59 11
1 1.00 1.00 1.00 1246
avg / total 0.99 0.99 0.99 1257
In [893]:
# 由上面看到基于rbf和linear的核函数，SVC的分类准确率并不是非常高。SVC模型的最佳特征向量数目是49-52个。
# 接下来我们用逻辑回归训练模型分类财报数据是否舞弊。
from sklearn.linear_model import LogisticRegressionCV
from sklearn.linear_model.coordinate_descent import ConvergenceWarning
warnings.filterwarnings(action='ignore', category=ConvergenceWarning) # 只拦截坐标下降法时聚合异常
# 使用之前整理的曼.惠特尼 U检验得到的样本。
X_train_lg_orgin = feature_test_sample.iloc[::2,:]
X_train_lg_orgin.reset_index(drop=True, inplace=True)
X_test_lg_orgin = feature_test_sample.iloc[1:feature_test_sample.shape[0]:2,:]
X_test_lg_orgin.reset_index(drop=True, inplace=True)
X_train_iter_lg = np.array(X_train_lg_orgin)
X_test_iter_lg = np.array(X_test_lg_orgin)
feature_selection_iteration_count = X_train_iter_lg.shape[1]-4 # 设置最多要删减多少个属性
print("feature_selection_iteration_count:", feature_selection_iteration_count)
reduce_count_per_iterate = 1
auc_values_lg = []
class_weight_lg = {1:1, -1:120}
# 经过多次试验，发现class_weight=20最合适。而且class_weight的margin很窄，稍大（如190）就会导致将很多非舞弊样本分类为舞弊样本。而该值
# 稍小，如170，则又使舞弊样本被分类为非舞弊样本。所以逻辑回归中，对于不均衡样本，超参class_weight是需要细心调试的。
cv_iter = CV_set(10, 0.5, 0.5)
lr = LogisticRegressionCV(Cs=np.logspace(-5,5,11), cv=cv_iter, class_weight=class_weight_lg, random_state=0)
for i in range(feature_selection_iteration_count):
for train_idx, test_idx in cv_iter.split(X_train_iter_lg, y_train):
X_train_cv, X_test_cv = X_train_iter_lg[train_idx], X_train_iter_lg[test_idx]
y_train_cv, y_test_cv = y_train[train_idx], y_train[test_idx]
lr.fit(X_train_cv, y_train_cv)
score = lr.score(X_train_cv, y_train_cv)
print("\n第%d次特征筛选：\n"%i)
print("R值，即准确率是：", score)
print("参数：",lr.coef_)
print("截距：",lr.intercept_)
#print("稀疏的特征个数比例是：%0.2f"%np.mean(lr.coef_.ravel()==0)*100)
print("训练结果用决策函数计算测试集的分类score值是：\n",lr.decision_function(X_test_iter_lg))
print("对测试集预测结果，用概率表示：\n", lr.predict_proba(X_test_iter_lg))
y_pred_iter_lg = lr.predict(X_test_iter_lg)
print("对测试集预测结果：\n", y_pred_iter_lg)
print("测试集预测结果中，舞弊样本的索引号：", np.where(y_pred_iter_lg==-1))
print("预测测试集中多少舞弊样本：", np.where(y_pred_iter_lg==-1)[0].size)
# 计算训练模型clf_op的AUC值，评估模型质量。
auc_value_iter_lg = auc(y_test, y_pred_iter_lg)
auc_values_lg.append(auc_value_iter_lg)
print("第%d次特征筛选，AUC值是："%i, auc_value_iter_lg)
print("X_train_iter_lg.shape is:",X_train_iter_lg.shape)
print("X_test_iter_lg.shape is:",X_test_iter_lg.shape)
X_train_iter_lg = feature_reduce(X_train_iter_lg, n=reduce_count_per_iterate)
X_test_iter_lg = feature_reduce(X_test_iter_lg, n=reduce_count_per_iterate)
# 计算特征数目
print("AUC值随特征数目变化：", auc_values_lg)
feature_count_lg = [X_train_lg_orgin.shape[1] - reduce_count_per_iterate*i for i in range(feature_selection_iteration_count)]
print("样本特征数目：", feature_count_lg)
auc_plot_by_feature_selection(feature_count_lg, auc_values_lg)
feature_selection_iteration_count: 25
第0次特征筛选：
R值，即准确率是： 0.9920382165605095
参数： [[-2.03213230e-16 6.76838587e-17 3.50660744e-18 5.28404801e-18
2.38926333e-18 1.70635782e-17 -7.87191586e-17 -4.22540598e-17
-1.18605367e-15 4.30985235e-17 -2.89865627e-14 -4.16543603e-17
-1.27580511e-16 1.40824450e-17 1.15945078e-17 -1.70226810e-16
3.85953638e-17 4.88761672e-10 -2.12277807e-10 -3.01744799e-17
-5.05665447e-17 -3.45748875e-17 -1.91307534e-17 -1.20871215e-17
-3.33256208e-16 6.59996573e-17 1.67449990e-14 1.61121093e-16
2.79810618e-16]]
截距： [-7.34495773e-17]
训练结果用决策函数计算测试集的分类score值是：
[0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
[[0.34659755 0.65340245]
[0.37495766 0.62504234]
[0.38626333 0.61373667]
...
[0.46335697 0.53664303]
[0.48904374 0.51095626]
[0.48963414 0.51036586]]
对测试集预测结果：
[1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第0次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 29)
X_test_iter_lg.shape is: (1257, 29)
第1次特征筛选：
R值，即准确率是： 0.9920382165605095
参数： [[-2.03213230e-16 6.76838587e-17 3.50660744e-18 5.28404801e-18
2.38926333e-18 1.70635782e-17 -7.87191586e-17 -4.22540598e-17
-1.18605367e-15 4.30985235e-17 -2.89865627e-14 -4.16543603e-17
-1.27580511e-16 1.40824450e-17 1.15945078e-17 -1.70226810e-16
3.85953638e-17 4.88761672e-10 -2.12277807e-10 -3.01744799e-17
-5.05665447e-17 -3.45748875e-17 -1.91307534e-17 -1.20871215e-17
-3.33256208e-16 6.59996573e-17 1.67449990e-14 1.61121093e-16]]
截距： [-7.34495773e-17]
训练结果用决策函数计算测试集的分类score值是：
[0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
[[0.34659755 0.65340245]
[0.37495766 0.62504234]
[0.38626333 0.61373667]
...
[0.46335697 0.53664303]
[0.48904374 0.51095626]
[0.48963414 0.51036586]]
对测试集预测结果：
[1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第1次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 28)
X_test_iter_lg.shape is: (1257, 28)
第2次特征筛选：
R值，即准确率是： 0.9920382165605095
参数： [[-2.03213230e-16 6.76838587e-17 3.50660744e-18 5.28404801e-18
2.38926333e-18 1.70635782e-17 -7.87191586e-17 -4.22540598e-17
-1.18605367e-15 4.30985235e-17 -2.89865627e-14 -4.16543603e-17
-1.27580511e-16 1.40824450e-17 1.15945078e-17 -1.70226810e-16
3.85953638e-17 4.88761672e-10 -2.12277807e-10 -3.01744799e-17
-5.05665447e-17 -3.45748875e-17 -1.91307534e-17 -1.20871215e-17
-3.33256208e-16 6.59996573e-17 1.67449990e-14]]
截距： [-7.34495773e-17]
训练结果用决策函数计算测试集的分类score值是：
[0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
[[0.34659755 0.65340245]
[0.37495766 0.62504234]
[0.38626333 0.61373667]
...
[0.46335697 0.53664303]
[0.48904374 0.51095626]
[0.48963414 0.51036586]]
对测试集预测结果：
[1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第2次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 27)
X_test_iter_lg.shape is: (1257, 27)
第3次特征筛选：
R值，即准确率是： 0.9920382165605095
参数： [[-2.03213211e-16 6.76838487e-17 3.50660716e-18 5.28404733e-18
2.38926291e-18 1.70635809e-17 -7.87191483e-17 -4.22540562e-17
-1.18605343e-15 4.30985205e-17 -2.89865569e-14 -4.16543567e-17
-1.27580509e-16 1.40824435e-17 1.15945074e-17 -1.70226801e-16
3.85953608e-17 4.88761672e-10 -2.12277806e-10 -3.01744743e-17
-5.05665392e-17 -3.45748797e-17 -1.91307506e-17 -1.20871510e-17
-3.33256185e-16 6.59996338e-17]]
截距： [-7.34495686e-17]
训练结果用决策函数计算测试集的分类score值是：
[0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
[[0.34659755 0.65340245]
[0.37495766 0.62504234]
[0.38626333 0.61373667]
...
[0.46335697 0.53664303]
[0.48904374 0.51095626]
[0.48963414 0.51036586]]
对测试集预测结果：
[1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第3次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 26)
X_test_iter_lg.shape is: (1257, 26)
第4次特征筛选：
R值，即准确率是： 0.9920382165605095
参数： [[-2.03213211e-16 6.76838487e-17 3.50660716e-18 5.28404733e-18
2.38926291e-18 1.70635809e-17 -7.87191483e-17 -4.22540562e-17
-1.18605343e-15 4.30985205e-17 -2.89865569e-14 -4.16543567e-17
-1.27580509e-16 1.40824435e-17 1.15945074e-17 -1.70226801e-16
3.85953608e-17 4.88761672e-10 -2.12277806e-10 -3.01744743e-17
-5.05665392e-17 -3.45748797e-17 -1.91307506e-17 -1.20871510e-17
-3.33256185e-16]]
截距： [-7.34495686e-17]
训练结果用决策函数计算测试集的分类score值是：
[0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
[[0.34659755 0.65340245]
[0.37495766 0.62504234]
[0.38626333 0.61373667]
...
[0.46335697 0.53664303]
[0.48904374 0.51095626]
[0.48963414 0.51036586]]
对测试集预测结果：
[1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第4次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 25)
X_test_iter_lg.shape is: (1257, 25)
第5次特征筛选：
R值，即准确率是： 0.9920382165605095
参数： [[-2.03213211e-16 6.76838487e-17 3.50660716e-18 5.28404733e-18
2.38926291e-18 1.70635809e-17 -7.87191483e-17 -4.22540562e-17
-1.18605343e-15 4.30985205e-17 -2.89865569e-14 -4.16543567e-17
-1.27580509e-16 1.40824435e-17 1.15945074e-17 -1.70226801e-16
3.85953608e-17 4.88761672e-10 -2.12277806e-10 -3.01744743e-17
-5.05665392e-17 -3.45748797e-17 -1.91307506e-17 -1.20871510e-17]]
截距： [-7.34495685e-17]
训练结果用决策函数计算测试集的分类score值是：
[0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
[[0.34659755 0.65340245]
[0.37495766 0.62504234]
[0.38626333 0.61373667]
...
[0.46335697 0.53664303]
[0.48904374 0.51095626]
[0.48963414 0.51036586]]
对测试集预测结果：
[1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第5次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 24)
X_test_iter_lg.shape is: (1257, 24)
第6次特征筛选：
R值，即准确率是： 0.9920382165605095
参数： [[-2.03213211e-16 6.76838487e-17 3.50660716e-18 5.28404733e-18
2.38926291e-18 1.70635809e-17 -7.87191483e-17 -4.22540562e-17
-1.18605343e-15 4.30985205e-17 -2.89865569e-14 -4.16543567e-17
-1.27580509e-16 1.40824435e-17 1.15945074e-17 -1.70226801e-16
3.85953608e-17 4.88761672e-10 -2.12277806e-10 -3.01744743e-17
-5.05665392e-17 -3.45748797e-17 -1.91307506e-17]]
截距： [-7.34495685e-17]
训练结果用决策函数计算测试集的分类score值是：
[0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
[[0.34659755 0.65340245]
[0.37495766 0.62504234]
[0.38626333 0.61373667]
...
[0.46335697 0.53664303]
[0.48904374 0.51095626]
[0.48963414 0.51036586]]
对测试集预测结果：
[1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第6次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 23)
X_test_iter_lg.shape is: (1257, 23)
第7次特征筛选：
R值，即准确率是： 0.9920382165605095
参数： [[-2.03213211e-16 6.76838487e-17 3.50660716e-18 5.28404733e-18
2.38926291e-18 1.70635809e-17 -7.87191483e-17 -4.22540562e-17
-1.18605343e-15 4.30985205e-17 -2.89865569e-14 -4.16543567e-17
-1.27580509e-16 1.40824435e-17 1.15945074e-17 -1.70226801e-16
3.85953608e-17 4.88761672e-10 -2.12277806e-10 -3.01744743e-17
-5.05665392e-17 -3.45748797e-17]]
截距： [-7.34495685e-17]
训练结果用决策函数计算测试集的分类score值是：
[0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
[[0.34659755 0.65340245]
[0.37495766 0.62504234]
[0.38626333 0.61373667]
...
[0.46335697 0.53664303]
[0.48904374 0.51095626]
[0.48963414 0.51036586]]
对测试集预测结果：
[1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第7次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 22)
X_test_iter_lg.shape is: (1257, 22)
第8次特征筛选：
R值，即准确率是： 0.9920382165605095
参数： [[-2.03213211e-16 6.76838487e-17 3.50660716e-18 5.28404733e-18
2.38926291e-18 1.70635809e-17 -7.87191483e-17 -4.22540562e-17
-1.18605343e-15 4.30985205e-17 -2.89865569e-14 -4.16543567e-17
-1.27580509e-16 1.40824435e-17 1.15945074e-17 -1.70226801e-16
3.85953608e-17 4.88761672e-10 -2.12277806e-10 -3.01744743e-17
-5.05665392e-17]]
截距： [-7.34495685e-17]
训练结果用决策函数计算测试集的分类score值是：
[0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
[[0.34659755 0.65340245]
[0.37495766 0.62504234]
[0.38626333 0.61373667]
...
[0.46335697 0.53664303]
[0.48904374 0.51095626]
[0.48963414 0.51036586]]
对测试集预测结果：
[1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第8次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 21)
X_test_iter_lg.shape is: (1257, 21)
第9次特征筛选：
R值，即准确率是： 0.9920382165605095
参数： [[-2.03213211e-16 6.76838487e-17 3.50660716e-18 5.28404733e-18
2.38926291e-18 1.70635809e-17 -7.87191483e-17 -4.22540562e-17
-1.18605343e-15 4.30985205e-17 -2.89865569e-14 -4.16543567e-17
-1.27580509e-16 1.40824435e-17 1.15945074e-17 -1.70226801e-16
3.85953608e-17 4.88761672e-10 -2.12277806e-10 -3.01744743e-17]]
截距： [-7.34495685e-17]
训练结果用决策函数计算测试集的分类score值是：
[0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
[[0.34659755 0.65340245]
[0.37495766 0.62504234]
[0.38626333 0.61373667]
...
[0.46335697 0.53664303]
[0.48904374 0.51095626]
[0.48963414 0.51036586]]
对测试集预测结果：
[1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第9次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 20)
X_test_iter_lg.shape is: (1257, 20)
第10次特征筛选：
R值，即准确率是： 0.9920382165605095
参数： [[-2.03213211e-16 6.76838487e-17 3.50660716e-18 5.28404733e-18
2.38926291e-18 1.70635809e-17 -7.87191483e-17 -4.22540562e-17
-1.18605343e-15 4.30985205e-17 -2.89865569e-14 -4.16543567e-17
-1.27580509e-16 1.40824435e-17 1.15945074e-17 -1.70226801e-16
3.85953608e-17 4.88761672e-10 -2.12277806e-10]]
截距： [-7.34495685e-17]
训练结果用决策函数计算测试集的分类score值是：
[0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
[[0.34659755 0.65340245]
[0.37495766 0.62504234]
[0.38626333 0.61373667]
...
[0.46335697 0.53664303]
[0.48904374 0.51095626]
[0.48963414 0.51036586]]
对测试集预测结果：
[1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第10次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 19)
X_test_iter_lg.shape is: (1257, 19)
第11次特征筛选：
R值，即准确率是： 0.9920382165605095
参数： [[-1.63181291e-17 5.41474443e-18 4.40610503e-19 5.78716077e-19
3.26803782e-19 1.08472473e-18 -7.29330105e-18 -5.29079810e-18
-1.33945114e-16 4.15486942e-18 -3.29584448e-15 -5.56546362e-18
-5.72889209e-18 1.40755425e-18 1.36714561e-18 -1.20572027e-17
3.57334169e-18 3.18882119e-10]]
截距： [-6.7029432e-18]
训练结果用决策函数计算测试集的分类score值是：
[0.53405393 0.53405393 0.53405393 ... 0.16935227 0.04104908 0.03177271]
对测试集预测结果，用概率表示：
[[0.36957187 0.63042813]
[0.36957187 0.63042813]
[0.36957187 0.63042813]
...
[0.45776283 0.54223717]
[0.48973917 0.51026083]
[0.49205749 0.50794251]]
对测试集预测结果：
[1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第11次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 18)
X_test_iter_lg.shape is: (1257, 18)
第12次特征筛选：
R值，即准确率是： 0.9952229299363057
参数： [[ 2.90678275 6.07572766 -6.6267034 4.13107283 -0.66780832
10.66195231 -17.88158946 -6.42733409 2.90437635 11.28403982
-0.26464584 -3.00052277 1.2720182 13.79337845 27.44652778
-2.67505526 36.16381365]]
截距： [-1.25913029]
训练结果用决策函数计算测试集的分类score值是：
[ 100.21992773 32.03247482 26.6574223 ... 52.80826386
-1767.9021739 4.65940144]
对测试集预测结果，用概率表示：
[[0.00000000e+00 1.00000000e+00]
[1.22124533e-14 1.00000000e+00]
[2.64743782e-12 1.00000000e+00]
...
[0.00000000e+00 1.00000000e+00]
[1.00000000e+00 0.00000000e+00]
[9.38325093e-03 9.90616749e-01]]
对测试集预测结果：
[ 1 1 1 ... 1 -1 1]
测试集预测结果中，舞弊样本的索引号： (array([ 93, 117, 121, 127, 142, 144, 244, 255, 332, 369, 370,
399, 454, 486, 521, 530, 613, 618, 641, 722, 724, 790,
792, 1163, 1222, 1246, 1247, 1251, 1252, 1253, 1255], dtype=int64),)
预测测试集中多少舞弊样本： 31
第12次特征筛选，AUC值是： 0.7626951699985407
X_train_iter_lg.shape is: (1257, 17)
X_test_iter_lg.shape is: (1257, 17)
第13次特征筛选：
R值，即准确率是： 0.9856687898089171
参数： [[ 0.69109826 2.60091091 -2.27073927 1.56427676 -0.25656371 2.02379431
-9.09107685 3.92446716 1.82598195 6.34243129 -0.28738847 -7.50652684
0.34966168 4.42949548 16.02301754 -0.44771323]]
截距： [4.92756848]
训练结果用决策函数计算测试集的分类score值是：
[ 56.22856572 15.39224594 12.65468487 ... 11.82417606 -634.96112975
5.25309847]
对测试集预测结果，用概率表示：
[[0.00000000e+000 1.00000000e+000]
[2.06648584e-007 9.99999793e-001]
[3.19255807e-006 9.99996807e-001]
...
[7.32524919e-006 9.99992675e-001]
[1.00000000e+000 1.73734123e-276]
[5.20406034e-003 9.94795940e-001]]
对测试集预测结果：
[ 1 1 1 ... 1 -1 1]
测试集预测结果中，舞弊样本的索引号： (array([ 93, 117, 121, 142, 144, 255, 262, 269, 279, 332, 370,
399, 454, 520, 521, 540, 549, 615, 618, 641, 658, 722,
787, 790, 792, 819, 1033, 1246, 1247, 1248, 1249, 1251, 1252,
1253, 1255], dtype=int64),)
预测测试集中多少舞弊样本： 35
第13次特征筛选，AUC值是： 0.8528016926893331
X_train_iter_lg.shape is: (1257, 16)
X_test_iter_lg.shape is: (1257, 16)
第14次特征筛选：
R值，即准确率是： 0.9920382165605095
参数： [[ 1.10966347 3.36182535 -1.26857313 3.67682606 0.9160366
2.70024122 -11.06080946 5.37871744 2.31485101 7.60230724
-0.35489103 -9.57055504 0.15280969 6.0489781 21.06954161]]
截距： [4.36023276]
训练结果用决策函数计算测试集的分类score值是：
[ 73.90184743 20.21317564 16.62460994 ... 14.30727592 -843.6865802
4.87124114]
对测试集预测结果，用概率表示：
[[0.00000000e+00 1.00000000e+00]
[1.66544134e-09 9.99999998e-01]
[6.02592350e-08 9.99999940e-01]
...
[6.11545465e-07 9.99999388e-01]
[1.00000000e+00 0.00000000e+00]
[7.60555960e-03 9.92394440e-01]]
对测试集预测结果：
[ 1 1 1 ... 1 -1 1]
测试集预测结果中，舞弊样本的索引号： (array([ 93, 117, 121, 142, 144, 255, 262, 269, 332, 370, 399,
454, 520, 521, 540, 549, 615, 618, 641, 658, 722, 792,
819, 1246, 1247, 1248, 1249, 1251, 1252, 1253, 1255], dtype=int64),)
预测测试集中多少舞弊样本： 31
第14次特征筛选，AUC值是： 0.8544068291259302
X_train_iter_lg.shape is: (1257, 15)
X_test_iter_lg.shape is: (1257, 15)
第15次特征筛选：
R值，即准确率是： 0.9585987261146497
参数： [[ -0.94255943 8.20555273 -1.99850615 14.5408816 -6.90213072
3.98868188 -19.5888829 11.98975571 3.57418258 18.60886652
-0.197935 -20.41689196 0.08116072 28.17375381]]
截距： [6.53345548]
训练结果用决策函数计算测试集的分类score值是：
[ 1.71530782e+01 2.17746121e+01 1.95315159e+01 ... -3.85021911e-01
-3.23083296e+03 -1.47095243e+01]
对测试集预测结果，用概率表示：
[[3.55232580e-08 9.99999964e-01]
[3.49466900e-10 1.00000000e+00]
[3.29283822e-09 9.99999997e-01]
...
[5.95083754e-01 4.04916246e-01]
[1.00000000e+00 0.00000000e+00]
[9.99999591e-01 4.09010632e-07]]
对测试集预测结果：
[ 1 1 1 ... -1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([ 10, 11, 14, 90, 93, 107, 114, 121, 142, 238, 254,
276, 277, 283, 324, 330, 331, 332, 370, 399, 518, 520,
521, 544, 545, 549, 568, 583, 635, 641, 675, 693, 694,
695, 697, 792, 819, 906, 976, 1033, 1041, 1068, 1071, 1089,
1222, 1234, 1246, 1247, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测测试集中多少舞弊样本： 54
第15次特征筛选，AUC值是： 0.845177294615497
X_train_iter_lg.shape is: (1257, 14)
X_test_iter_lg.shape is: (1257, 14)
第16次特征筛选：
R值，即准确率是： 0.9777070063694268
参数： [[ 6.90296445e-04 -4.30248401e-04 1.32115705e-04 9.71223791e-05
7.72296728e-05 1.00015708e-03 -9.03539454e-06 -1.07993055e-03
4.42515211e-04 8.82194577e-04 -3.75018958e-02 -1.48433617e-03
1.16165252e-03]]
截距： [1.11912012]
训练结果用决策函数计算测试集的分类score值是：
[0.90505535 0.96957078 0.95774776 ... 1.00610027 0.87704741 0.98531182]
对测试集预测结果，用概率表示：
[[0.28801273 0.71198727]
[0.27496606 0.72503394]
[0.27732936 0.72267064]
...
[0.26774373 0.73225627]
[0.29379 0.70621 ]
[0.27183907 0.72816093]]
对测试集预测结果：
[1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([ 67, 90, 98, 99, 114, 143, 144, 270, 277, 331, 332,
348, 370, 399, 469, 520, 521, 540, 549, 615, 641, 657,
658, 792, 793, 794, 819, 906, 1032, 1033, 1152, 1246, 1247,
1248], dtype=int64),)
预测测试集中多少舞弊样本： 34
第16次特征筛选，AUC值是： 0.6239238289800088
X_train_iter_lg.shape is: (1257, 13)
X_test_iter_lg.shape is: (1257, 13)
第17次特征筛选：
R值，即准确率是： 0.9617834394904459
参数： [[ -0.58295976 6.18979894 1.16050985 41.27631055 4.28454072
2.22066831 -23.46605223 11.43346698 3.35590023 18.56128559
-0.18461593 -18.24071324]]
截距： [8.24100795]
训练结果用决策函数计算测试集的分类score值是：
[ 15.18533607 19.40327627 17.22909151 ... 14.02310121 -495.16622786
-6.018963 ]
对测试集预测结果，用概率表示：
[[2.54151237e-007 9.99999746e-001]
[3.74338227e-009 9.99999996e-001]
[3.29230918e-008 9.99999967e-001]
...
[8.12538920e-007 9.99999187e-001]
[1.00000000e+000 8.95446440e-216]
[9.97573711e-001 2.42628928e-003]]
对测试集预测结果：
[ 1 1 1 ... 1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([ 10, 12, 14, 90, 91, 107, 114, 121, 142, 238, 242,
276, 277, 279, 280, 324, 330, 331, 332, 370, 399, 518,
519, 520, 521, 544, 545, 549, 635, 641, 674, 675, 694,
695, 696, 697, 736, 792, 794, 819, 906, 976, 1068, 1071,
1089, 1137, 1234, 1246, 1247, 1251, 1252, 1253, 1255, 1256],
dtype=int64),)
预测测试集中多少舞弊样本： 54
第17次特征筛选，AUC值是： 0.7993214650518022
X_train_iter_lg.shape is: (1257, 12)
X_test_iter_lg.shape is: (1257, 12)
第18次特征筛选：
R值，即准确率是： 0.9665605095541401
参数： [[ -0.78079273 -2.78450005 -4.91453911 61.33340993 -5.89367459
-8.31836124 -63.44756969 -2.75846651 4.86560041 43.11324444
-0.21361642]]
截距： [41.24317585]
训练结果用决策函数计算测试集的分类score值是：
[ 24.03189053 30.9432141 27.13021845 ... 17.01761274
-1262.79395149 -8.79976428]
对测试集预测结果，用概率表示：
[[3.65665276e-11 1.00000000e+00]
[3.64153152e-14 1.00000000e+00]
[1.65001346e-12 1.00000000e+00]
...
[4.06766026e-08 9.99999959e-01]
[1.00000000e+00 0.00000000e+00]
[9.99849254e-01 1.50745882e-04]]
对测试集预测结果：
[ 1 1 1 ... 1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([ 11, 90, 107, 114, 121, 144, 146, 238, 276, 277, 279,
280, 324, 331, 332, 347, 348, 349, 370, 404, 481, 518,
519, 520, 521, 540, 544, 545, 546, 549, 583, 614, 615,
641, 657, 666, 674, 675, 694, 695, 696, 697, 722, 723,
736, 792, 793, 794, 819, 906, 976, 1033, 1050, 1089, 1160,
1197, 1207, 1234, 1247, 1251, 1252, 1253, 1255, 1256], dtype=int64),)
预测测试集中多少舞弊样本： 64
第18次特征筛选，AUC值是： 0.7494527943966145
X_train_iter_lg.shape is: (1257, 11)
X_test_iter_lg.shape is: (1257, 11)
第19次特征筛选：
R值，即准确率是： 0.8805732484076433
参数： [[ 1.1524155 0.6056264 0.31596518 160.47661429 -56.75168263
-3.58384784 -20.22926331 -1.62450753 0.61208183 19.02000326]]
截距： [6.69458062]
训练结果用决策函数计算测试集的分类score值是：
[ 16.05360088 17.97075293 17.08168156 ... -10.92759498 -483.64929177
-26.47762726]
对测试集预测结果，用概率表示：
[[1.06661989e-007 9.99999893e-001]
[1.56819895e-008 9.99999984e-001]
[3.81522310e-008 9.99999962e-001]
...
[9.99982044e-001 1.79555207e-005]
[1.00000000e+000 8.99044955e-211]
[1.00000000e+000 3.16892957e-012]]
对测试集预测结果：
[ 1 1 1 ... -1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([ 10, 21, 35, 36, 37, 38, 39, 40, 58, 61, 62,
67, 73, 74, 75, 90, 107, 111, 112, 113, 114, 115,
117, 121, 143, 144, 145, 146, 158, 163, 165, 181, 182,
183, 185, 195, 196, 200, 201, 214, 215, 233, 236, 246,
247, 262, 264, 269, 276, 281, 288, 300, 319, 324, 330,
331, 332, 338, 347, 349, 358, 359, 366, 368, 369, 370,
403, 404, 429, 430, 431, 432, 433, 442, 463, 468, 469,
471, 477, 481, 503, 516, 543, 544, 545, 555, 565, 567,
611, 613, 614, 615, 616, 624, 630, 635, 641, 661, 665,
666, 667, 675, 687, 696, 697, 722, 723, 735, 736, 742,
777, 778, 787, 788, 790, 794, 795, 797, 883, 884, 885,
951, 954, 955, 965, 966, 967, 1012, 1014, 1017, 1050, 1051,
1061, 1071, 1089, 1094, 1095, 1096, 1097, 1098, 1129, 1148, 1160,
1222, 1230, 1234, 1241, 1242, 1243, 1246, 1247, 1248, 1249, 1251,
1252, 1253, 1254, 1255, 1256], dtype=int64),)
预测测试集中多少舞弊样本： 159
第19次特征筛选，AUC值是： 0.8947541222822121
X_train_iter_lg.shape is: (1257, 10)
X_test_iter_lg.shape is: (1257, 10)
第20次特征筛选：
R值，即准确率是： 0.8742038216560509
参数： [[ 0.60152679 -0.52057869 0.85646571 196.98385641 -111.05405471
1.41935943 -19.0220167 -1.47478524 0.3022195 ]]
截距： [11.55344688]
训练结果用决策函数计算测试集的分类score值是：
[ 10.65900105 9.61027701 10.43659687 ... -10.8983353 -442.72882198
-23.33268934]
对测试集预测结果，用概率表示：
[[2.34879148e-005 9.99976512e-001]
[6.70317583e-005 9.99932968e-001]
[2.93380217e-005 9.99970662e-001]
...
[9.99981511e-001 1.84886452e-005]
[1.00000000e+000 5.31270419e-193]
[1.00000000e+000 7.35769483e-011]]
对测试集预测结果：
[ 1 1 1 ... -1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([ 10, 21, 22, 30, 32, 34, 35, 36, 37, 38, 39,
40, 57, 58, 61, 62, 65, 67, 73, 74, 75, 90,
107, 108, 111, 113, 115, 117, 121, 123, 126, 143, 144,
145, 158, 163, 165, 181, 182, 183, 185, 186, 195, 196,
197, 200, 201, 214, 216, 233, 236, 243, 244, 245, 246,
247, 264, 276, 281, 283, 288, 299, 300, 301, 319, 330,
338, 339, 340, 349, 358, 366, 368, 369, 370, 429, 430,
431, 432, 433, 442, 449, 457, 463, 469, 471, 477, 481,
492, 493, 503, 516, 517, 529, 544, 555, 567, 583, 590,
599, 600, 601, 611, 612, 613, 614, 615, 618, 624, 630,
635, 641, 651, 657, 658, 661, 664, 665, 666, 667, 674,
675, 687, 696, 697, 712, 718, 722, 723, 736, 742, 775,
777, 778, 792, 793, 794, 795, 797, 883, 884, 885, 910,
911, 947, 948, 949, 950, 951, 954, 955, 956, 965, 966,
967, 1012, 1014, 1037, 1071, 1074, 1089, 1094, 1095, 1096, 1097,
1098, 1148, 1188, 1222, 1230, 1231, 1234, 1239, 1241, 1246, 1247,
1248, 1249, 1251, 1252, 1253, 1254, 1255, 1256], dtype=int64),)
预测测试集中多少舞弊样本： 184
第20次特征筛选，AUC值是： 0.8847220195534802
X_train_iter_lg.shape is: (1257, 9)
X_test_iter_lg.shape is: (1257, 9)
第21次特征筛选：
R值，即准确率是： 0.8821656050955414
参数： [[ 0.77287046 0.41987355 0.69778946 264.8291387 -207.40292127
2.45240204 -8.67664032 -2.20804846]]
截距： [7.02374223]
训练结果用决策函数计算测试集的分类score值是：
[ 9.64060381 9.06812659 9.53831334 ... -7.93185357 -217.49845298
-20.59143137]
对测试集预测结果，用概率表示：
[[6.50295459e-05 9.99934970e-01]
[1.15269020e-04 9.99884731e-01]
[7.20330599e-05 9.99927967e-01]
...
[9.99641009e-01 3.58991219e-04]
[1.00000000e+00 3.48034301e-95]
[9.99999999e-01 1.14091944e-09]]
对测试集预测结果：
[ 1 1 1 ... -1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([ 10, 20, 21, 22, 34, 35, 36, 37, 38, 39, 40,
57, 58, 59, 61, 62, 73, 74, 75, 90, 107, 108,
109, 110, 111, 121, 126, 142, 144, 145, 146, 158, 162,
163, 165, 181, 182, 183, 185, 186, 195, 196, 197, 200,
229, 233, 236, 243, 244, 246, 247, 264, 276, 278, 281,
283, 288, 300, 301, 309, 319, 320, 330, 338, 339, 349,
358, 366, 368, 369, 370, 429, 430, 431, 432, 433, 442,
449, 455, 457, 463, 469, 471, 477, 483, 484, 485, 492,
493, 500, 503, 516, 518, 529, 544, 555, 563, 584, 599,
602, 611, 612, 613, 615, 617, 624, 629, 630, 635, 651,
653, 657, 658, 659, 660, 661, 665, 666, 667, 674, 675,
696, 697, 712, 722, 723, 736, 742, 777, 792, 793, 795,
797, 829, 849, 910, 911, 931, 947, 948, 949, 950, 951,
954, 955, 956, 965, 966, 970, 971, 1071, 1074, 1094, 1095,
1096, 1097, 1098, 1156, 1239, 1246, 1247, 1248, 1249, 1251, 1252,
1253, 1254, 1255, 1256], dtype=int64),)
预测测试集中多少舞弊样本： 169
第21次特征筛选，AUC值是： 0.8907412811907194
X_train_iter_lg.shape is: (1257, 8)
X_test_iter_lg.shape is: (1257, 8)
第22次特征筛选：
R值，即准确率是： 0.8805732484076433
参数： [[ 0.91268663 -0.70857842 1.27340634 216.87273121 -144.71342152
2.29058762 -7.17032919]]
截距： [2.2626857]
训练结果用决策函数计算测试集的分类score值是：
[ 7.75050062 8.14741097 8.20684924 ... -9.19938324 -201.72689448
-21.11857446]
对测试集预测结果，用概率表示：
[[4.30341684e-04 9.99569658e-01]
[2.89400097e-04 9.99710600e-01]
[2.72704441e-04 9.99727296e-01]
...
[9.99898908e-01 1.01091517e-04]
[1.00000000e+00 2.46106383e-88]
[9.99999999e-01 6.73472158e-10]]
对测试集预测结果：
[ 1 1 1 ... -1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([ 10, 12, 20, 21, 22, 34, 35, 36, 37, 38, 39,
40, 57, 58, 59, 62, 67, 73, 74, 75, 90, 93,
107, 108, 109, 110, 111, 115, 117, 121, 144, 145, 158,
162, 163, 165, 177, 182, 183, 185, 186, 195, 196, 199,
200, 201, 202, 216, 232, 233, 236, 243, 244, 246, 247,
264, 270, 276, 277, 278, 281, 283, 288, 299, 300, 301,
309, 330, 333, 338, 340, 349, 351, 358, 359, 366, 368,
369, 370, 429, 430, 431, 432, 433, 434, 442, 454, 455,
456, 457, 463, 469, 471, 477, 483, 484, 485, 492, 493,
500, 501, 502, 503, 516, 544, 555, 558, 563, 584, 599,
611, 613, 614, 615, 616, 617, 618, 624, 629, 630, 635,
651, 657, 658, 661, 665, 666, 667, 675, 687, 712, 722,
723, 736, 742, 775, 777, 792, 793, 795, 797, 815, 829,
849, 910, 911, 947, 951, 953, 954, 955, 956, 966, 970,
971, 1017, 1037, 1061, 1071, 1074, 1089, 1094, 1095, 1096, 1097,
1098, 1148, 1156, 1222, 1234, 1239, 1246, 1247, 1248, 1249, 1251,
1252, 1253, 1254, 1255, 1256], dtype=int64),)
预测测试集中多少舞弊样本： 181
第22次特征筛选，AUC值是： 0.8859258718809281
X_train_iter_lg.shape is: (1257, 7)
X_test_iter_lg.shape is: (1257, 7)
第23次特征筛选：
R值，即准确率是： 0.8710191082802548
参数： [[ 0.98587444 0.32231535 1.41598978 221.50234783 -148.61203776
2.22240724]]
截距： [-3.36423593]
训练结果用决策函数计算测试集的分类score值是：
[ 6.87684649 7.2300183 7.30282292 ... -8.69396149 -63.62728446
-19.25029245]
对测试集预测结果，用概率表示：
[[1.03032875e-03 9.98969671e-01]
[7.23983073e-04 9.99276017e-01]
[6.73180993e-04 9.99326819e-01]
...
[9.99832433e-01 1.67566701e-04]
[1.00000000e+00 2.32820630e-28]
[9.99999996e-01 4.36218633e-09]]
对测试集预测结果：
[ 1 1 1 ... -1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([ 10, 11, 12, 20, 21, 22, 24, 34, 35, 36, 37,
38, 39, 40, 57, 58, 59, 62, 67, 73, 74, 75,
90, 93, 107, 108, 109, 110, 115, 117, 121, 144, 145,
158, 162, 163, 165, 177, 181, 182, 183, 185, 186, 195,
196, 199, 200, 201, 202, 214, 231, 232, 233, 236, 243,
244, 246, 247, 264, 269, 276, 281, 283, 287, 288, 299,
300, 301, 309, 330, 333, 334, 338, 340, 349, 351, 358,
359, 366, 368, 369, 370, 411, 429, 430, 431, 432, 433,
434, 442, 454, 455, 456, 457, 463, 469, 471, 477, 492,
493, 500, 501, 502, 503, 509, 516, 532, 544, 555, 558,
584, 599, 611, 613, 615, 616, 617, 623, 624, 629, 630,
635, 641, 651, 653, 658, 659, 660, 661, 665, 666, 667,
675, 712, 718, 722, 723, 736, 742, 756, 775, 777, 787,
792, 793, 795, 797, 815, 816, 829, 849, 876, 910, 911,
931, 947, 951, 953, 954, 955, 956, 966, 970, 971, 1017,
1037, 1061, 1071, 1074, 1089, 1094, 1095, 1096, 1097, 1098, 1137,
1148, 1154, 1156, 1220, 1222, 1239, 1244, 1246, 1247, 1248, 1249,
1251, 1252, 1254, 1255, 1256], dtype=int64),)
预测测试集中多少舞弊样本： 192
第23次特征筛选，AUC值是： 0.8356559171165913
X_train_iter_lg.shape is: (1257, 6)
X_test_iter_lg.shape is: (1257, 6)
第24次特征筛选：
R值，即准确率是： 0.8550955414012739
参数： [[ 1.07335221 0.50095129 1.33200667 236.08507457 -156.65038928]]
截距： [-2.72710102]
训练结果用决策函数计算测试集的分类score值是：
[ 6.89628971 6.031193 6.66133615 ... -9.33872273 -68.43682313
-20.05316857]
对测试集预测结果，用概率表示：
[[1.01050938e-03 9.98989491e-01]
[2.39686717e-03 9.97603133e-01]
[1.27780084e-03 9.98722199e-01]
...
[9.99912056e-01 8.79439679e-05]
[1.00000000e+00 1.89786517e-30]
[9.99999998e-01 1.95442741e-09]]
对测试集预测结果：
[ 1 1 1 ... -1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([ 10, 11, 12, 20, 21, 22, 24, 34, 35, 36, 37,
38, 39, 40, 57, 58, 59, 62, 65, 67, 75, 90,
107, 108, 109, 110, 115, 117, 121, 127, 144, 145, 158,
162, 163, 165, 177, 180, 181, 182, 183, 185, 186, 195,
199, 200, 201, 202, 231, 232, 233, 236, 243, 244, 246,
247, 264, 269, 276, 281, 283, 286, 287, 288, 300, 301,
309, 330, 332, 333, 334, 338, 349, 351, 358, 359, 366,
368, 369, 370, 429, 430, 431, 432, 433, 434, 442, 449,
454, 455, 456, 457, 463, 469, 471, 477, 492, 493, 500,
502, 503, 509, 516, 532, 544, 555, 558, 599, 611, 612,
613, 615, 617, 623, 624, 629, 630, 635, 641, 651, 653,
658, 659, 660, 661, 665, 666, 667, 675, 696, 697, 712,
718, 722, 723, 736, 742, 775, 777, 778, 787, 790, 792,
793, 795, 797, 815, 829, 874, 876, 910, 911, 931, 947,
951, 953, 954, 955, 956, 966, 970, 971, 1017, 1037, 1061,
1071, 1074, 1089, 1094, 1095, 1096, 1097, 1098, 1137, 1148, 1154,
1156, 1160, 1220, 1222, 1239, 1241, 1244, 1246, 1247, 1248, 1249,
1251, 1252, 1254, 1255, 1256], dtype=int64),)
预测测试集中多少舞弊样本： 192
第24次特征筛选，AUC值是： 0.8356559171165913
X_train_iter_lg.shape is: (1257, 5)
X_test_iter_lg.shape is: (1257, 5)
AUC值随特征数目变化： [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.7626951699985407, 0.8528016926893331, 0.8544068291259302, 0.845177294615497, 0.6239238289800088, 0.7993214650518022, 0.7494527943966145, 0.8947541222822121, 0.8847220195534802, 0.8907412811907194, 0.8859258718809281, 0.8356559171165913, 0.8356559171165913]
样本特征数目： [29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5]
In [895]:
# 通过上面试验，发现特征选8个时，分类器性能最佳。逻辑回归的sklearn库有个小问题，即不能将模型保留下来，如果在交叉验证中得到一个最优模型，
# 则如果仅仅以特征筛选的样本重新训练模型，则新得到的逻辑回归模型并不是最优的，性能往往下降。所以特征筛选后，依然需要一次
# LogisticRegressionCV，交叉验证得到最优模型。
lr_op = LogisticRegressionCV(Cs=np.logspace(-5,5,11), cv=cv_iter, class_weight=class_weight_lg, random_state=0, solver='lbfgs')
auc_lg = np.array(auc_values_lg)
best_param_op_idx_lg = np.max(np.where(auc_lg==np.max(auc_lg))[0])
print("最佳超参的索引best_param_op_idx是：", best_param_op_idx_lg)
best_feature_count_lg = feature_count_lg[best_param_op_idx_lg]
X_train_op = np.array(X_train_lg_orgin.iloc[:,:best_feature_count_lg])
y_train_op = y_train
X_test_op = np.array(X_test_lg_orgin.iloc[:,:best_feature_count_lg])
y_test_op = y_test
print("逻辑回归模型中，测试集中标签为+1的样本数是：",X_test_op)
for train_idx, test_idx in cv_iter.split(X_train_op, y_train_op):
X_train_cv, X_test_cv = X_train_op[train_idx], X_train_op[test_idx]
y_train_cv, y_test_cv = y_train_op[train_idx], y_train_op[test_idx]
lr_op.fit(X_train_cv, y_train_cv)
print("训练的最优逻辑回归模型的参数：\n", lr_op.coef_)
print("训练的最优逻辑回归模型的截距：\n", lr_op.intercept_)
y_pred_lg = lr_op.predict(X_test_op)
y_pred_lg_prob = lr_op.predict_proba(X_test_op)
print("用训练好的逻辑回归模型，预测测试集的结果是：\n", y_pred_lg)
print("测试集中预测为舞弊样本的索引号是：", np.where(y_pred_lg==-1))
print("测试集中预测为舞弊样本的个数有：", np.where(y_pred_lg==-1)[0].size)
# 计算训练模型clf_op的AUC值，评估模型质量。
auc_value_op_lg = auc(y_test_op, y_pred_lg)
print("最优逻辑回归模型得到的AUC值：", auc_value_op_lg)
# 打印逻辑回归的分类效果报告
print("用最优逻辑回归模型，得到的分类结果评估报告如下：\n", classification_report(y_test_op, y_pred_lg))
# 通过逻辑回归的调试，发现逻辑回归并不比SVC性能差很多，但是计算更加简便，而且计算快速，综合表现很不错。
最佳超参的索引best_param_op_idx是： 19
逻辑回归模型中，测试集中标签为+1的样本数是： [[ 5.00000000e+00 1.50905500e+00 1.33241000e-01 ... 7.92489000e-01
9.04582000e-01 4.31495000e-01]
[ 5.00000000e+00 1.55607000e+00 1.53912000e-01 ... 1.38748100e+00
8.35262000e-01 7.39164000e-01]
[ 5.00000000e+00 1.63892600e+00 1.23446000e-01 ... 1.18496600e+00
7.85876000e-01 5.75827000e-01]
...
[ 4.00000000e+00 2.37979000e-01 -1.20878000e-01 ... 9.75833000e-01
6.92581600e+00 1.99014000e-01]
[ 3.00000000e+00 -9.50609000e-01 -1.83469000e-01 ... 2.02900000e-03
5.52993977e+00 7.30000000e-04]
[ 1.00000000e+00 -9.16360000e-02 -4.00649000e-01 ... 2.18884000e-01
5.52993977e+00 1.24130000e-02]]
训练的最优逻辑回归模型的参数：
[[ 1.1524155 0.6056264 0.31596518 160.47661429 -56.75168263
-3.58384784 -20.22926331 -1.62450753 0.61208183 19.02000326]]
训练的最优逻辑回归模型的截距：
[6.69458062]
用训练好的逻辑回归模型，预测测试集的结果是：
[ 1 1 1 ... -1 -1 -1]
测试集中预测为舞弊样本的索引号是： (array([ 10, 21, 35, 36, 37, 38, 39, 40, 58, 61, 62,
67, 73, 74, 75, 90, 107, 111, 112, 113, 114, 115,
117, 121, 143, 144, 145, 146, 158, 163, 165, 181, 182,
183, 185, 195, 196, 200, 201, 214, 215, 233, 236, 246,
247, 262, 264, 269, 276, 281, 288, 300, 319, 324, 330,
331, 332, 338, 347, 349, 358, 359, 366, 368, 369, 370,
403, 404, 429, 430, 431, 432, 433, 442, 463, 468, 469,
471, 477, 481, 503, 516, 543, 544, 545, 555, 565, 567,
611, 613, 614, 615, 616, 624, 630, 635, 641, 661, 665,
666, 667, 675, 687, 696, 697, 722, 723, 735, 736, 742,
777, 778, 787, 788, 790, 794, 795, 797, 883, 884, 885,
951, 954, 955, 965, 966, 967, 1012, 1014, 1017, 1050, 1051,
1061, 1071, 1089, 1094, 1095, 1096, 1097, 1098, 1129, 1148, 1160,
1222, 1230, 1234, 1241, 1242, 1243, 1246, 1247, 1248, 1249, 1251,
1252, 1253, 1254, 1255, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 159
最优逻辑回归模型得到的AUC值： 0.8947541222822121
用最优逻辑回归模型，得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.06 0.91 0.12 11
1 1.00 0.88 0.94 1246
avg / total 0.99 0.88 0.93 1257
In [868]:
# 开始朴素贝叶斯模型的相关训练。朴素贝叶斯也使用feature_test_sample样本。
feature_test_sample['label'] = merge_samples['label']
cols_bayes = fishvalue(feature_test_sample, ascending=True)
print(cols_bayes)
0 净资产收益率增长率B 0.019121 利息保障倍数B 0.022635 利润总额增长率B 0.064017 营业利润现金净含量 0.078418 净利润增长率B 0.122319 所有者权益增长率B 0.137061 总资产增长率B 0.156792 经营杠杆 0.159151 固定资产增长率B 0.161007 已流通股份 0.178975 股本总数 0.185301 存货与收入比 0.192904 财务杠杆 0.194951 营业收入增长率B 0.203102 营业利润率 0.232845 综合杠杆 0.238151 速动比率 0.250274 现金及现金等价物周转率B 0.326825 现金比率 0.340413 有形净值债务率 0.349547 流动比率 0.349765 资产负债率 0.376918 保守速动比率 0.398686 资产报酬率B 0.406682 总资产净利润率（ROA）B 0.443927 营业收入现金净含量 0.460457 权益对负债比率 0.518996 监事总规模 0.548191 dtype: float64
In [869]:
# 手工区分彼此独立的特征向量中连续型和离散型特征。
# 人工将样本集中连续型特征向量和离散型特征向量分成2个子集。
continuous_cols = ['净资产收益率增长率B', '利息保障倍数B', '利润总额增长率B', '营业利润现金净含量', '净利润增长率B', '所有者权益增长率B',
'总资产增长率B', '经营杠杆', '固定资产增长率B', '存货与收入比', '财务杠杆', '营业收入增长率B', '营业利润率',
'综合杠杆', '速动比率', '现金及现金等价物周转率B', '现金比率', '有形净值债务率', '流动比率', '资产负债率',
'保守速动比率', '资产报酬率B', '总资产净利润率（ROA）B', '营业收入现金净含量', '权益对负债比率']
discrete_cols = ['已流通股份', '股本总数', '监事总规模']
# 将之前做曼.惠特尼 U检验得到的样本，分成连续型特征向量和离散型特征向量2个子集。
sample_continuous_independence = feature_test_sample.loc[:, continuous_cols]
sample_discrete_independence = feature_test_sample.loc[:, discrete_cols]
# 对连续型特征向量，直接计算相关去重多重共线性。针对离散型特征向量，将其标准化，按照连续型特征向量求相关的办法去除多重共线性。
continuous_multicollinearity_result = multicollinearity_check(sample_continuous_independence, coef_corr_crit = 0.7)
sample_continuous_bayes = sample_continuous_independence.loc[:,continuous_multicollinearity_result[0].columns]
print("朴素贝叶斯的训练集中连续型特征向量的属性有：\n", sample_continuous_bayes.columns)
# 离散型特征向量，先进行标准化，再计算相关。
aver = sample_discrete_independence.mean()
stds = sample_discrete_independence.std()
sample_discrete_independence_scale = (sample_discrete_independence - aver)/stds
discrete_multicollinearity_result = multicollinearity_check(sample_discrete_independence_scale, coef_corr_crit = 0.7)
sample_discrete_bayes = sample_discrete_independence.loc[:, discrete_multicollinearity_result[0].columns]
print("朴素贝叶斯的训练集中离散型特征向量的属性有：\n", sample_discrete_bayes.columns)
各列相关系数：
[0.00144216817961587, 0.266362863022752, -0.04074081340728008, 0.3248162754573849, 0.011097628425079836, 0.032890338448322065, 0.003663627015528724, 0.02172370654785139, -0.02347486533313489, 0.013794121398880949, -0.06246712000657116, 0.01348838763344581, 0.013455136611883415, 0.01820656159044208, -0.016283775477401903, 0.025340707609602428, -0.009817489335131551, 0.01420655117334823, -0.006239177600711663, 0.024353411616673374, 0.06937489528394797, 0.0722901172284574, -0.02118505420247058, 0.015827419532336007, -0.0003590327239245374, -0.0003401268785604493, -0.00018502825268436386, -0.000384549403614251, -0.002060518109898519, -0.0008264962266667719, -0.0020013457633922135, -0.008693289032612074, -0.004406927929681737, -0.0020520883766335174, 0.001103432816563823, -0.003628814769132249, 0.009570260684524498, -0.005306179800194085, 0.006938397129284019, -0.0038544125044864858, 0.006573127884154006, -0.009659038729809973, 0.010038255463598891, 0.0053281880897566615, 0.006983582915083806, -0.00027957911490396396, 0.018823642518688434, -0.012549091721170403, 0.9725515012027429, 0.14807203517308698, 0.13944214828650867, -0.004267486408600675, 0.030607844126020606, -0.006508510022997263, -0.006388727261122722, 0.06984877485395427, -0.005201524483254323, -0.006934273415922588, -0.008256690889171492, 0.004192557304157075, -0.007656630794410142, -0.0039958287163785345, -0.010102545626854127, 0.003818236439199596, -0.00842998974521528, 0.020816313779038583, 0.020350867104219177, -0.0090762999551118, -0.005668360033383939, -0.014986158143714616, -0.0002132482411288956, -0.010169762843758859, 0.007276234941107044, -0.005067061254795126, -0.041628492597475385, 0.04790817525339251, -0.006127387426945903, 6.270245535944578e-06, 0.06421266015479014, -0.0032648510750246168, -0.007023382026360469, 0.007322034577808228, 0.003777523747092891, -0.013309872087904287, 0.003203481736913676, 0.003024343055534874, -0.005953036504407012, -0.008496567605719731, 0.1391379374012218, -0.010221376657693172, 0.14922923978314098, 0.17160966290860188, -0.004833484872908417, 0.049053430916275655, -0.01374038288254288, -0.004266053582221631, 0.11673357450693018, -0.004952368349280404, -0.004949485794770213, -0.0063778749942128175, 0.003226407350341479, -0.006107261699088929, -0.006222838505865594, -0.008961077219394507, 0.003040700415343681, -0.0055481722863908135, 0.034475738734649354, 0.03424034296335226, -0.010359403006654697, -0.004761459912935849, 0.2696768685170896, -0.002553145920772284, 0.033505857356337966, -0.04086910771562955, -0.0062951118237116015, 0.06400202587892438, 0.028072360511326774, -0.007201115952895933, 0.169928904799294, -0.010736496392929838, 0.259042891007241, -0.0059296210547977405, 0.15437231328368967, -0.02518000908125928, 0.20411679808344202, 0.0076871673196163585, 0.009409119180621612, 0.021203069814151616, 0.10307979840982669, -0.00011592005774788982, 0.21273782610018732, 0.12282773404090848, -0.022223534267493576, 0.4726529864405968, 0.016922998135785655, -0.02266231749601387, 0.06550198636235671, -0.006393551161781229, 0.11665731509084212, 0.0070092724069018226, 0.06110469731060249, 0.0013731788768856685, 0.08632476137583274, 0.016184978502440755, 0.016977299821006233, -0.07590437433800526, 0.02686687412085318, -0.00240230293450678, -0.02379487285279039, 0.04678060854620868, 0.001725551591208602, -0.0008549217370303899, -0.012787018310953847, -0.04541974719794733, 0.015307926981346568, -0.03789076347446676, 0.0288709161804415, -0.05319984424489668, 0.011714283812346822, -0.047306208660776096, -0.015086060766012625, -0.025630085674357414, 0.008846802652918083, -0.04614819011260139, 0.05772696593968117, -0.016352441047452114, 0.3156460562816733, 0.005745434353271657, -0.018668734764679257, -0.020571672323064973, -0.006746813050371479, -0.024203396635658545, 0.006461863313027115, -0.019144892440518792, 0.009753583247744167, -0.017041424188425046, 0.008759100213225714, 0.0096416667790772, 0.004737653031892205, -0.043216279073151065, -0.0271136481374564, 0.020466800991964722, 0.02361596095159118, -0.047243018052821203, -0.12693297425333314, -0.08042645886996916, -0.08938899775325526, 0.019151677595085154, 0.02313960202029894, 0.04277018693753762, -0.12807386553681432, -0.06931532293794183, -0.06195222878266794, -0.13519512000653472, -0.07773441873883709, -0.024328172029809147, -0.0074243092224864065, 0.821488516957293, -0.1290398207726612, 0.059715168505606674, -0.1056904973882776, 0.05595521584844768, -0.14488778187340273, 0.053630639652356656, -0.12481002506211494, -0.05013480106852509, -0.09890164343238889, 0.006915677640370861, -0.1300794560565374, 0.013538366586503935, -0.02338887794460296, -0.026991343477040963, 0.014722157535483367, -0.02719678963332585, 0.026238972556650097, -0.02422050869851953, 0.006369971258984416, -0.02484923868534241, 0.024589557358064198, 0.022284741276938816, -0.024901926641727086, -0.04300529133404731, -0.0061119434563316095, 0.04630078813110222, -0.011897426918339447, 0.04279445657476758, -0.015046978363328077, 0.04722545584126505, -0.565724314028529, 0.041954765348374834, 0.2645934705371692, 0.26120440934952227, 0.03717516101151497, 0.058389404685595524, -0.08937078987040081, 0.048091597774508894, -0.07628905060670857, 0.05036733667455726, -0.10845483996363617, 0.03146873552543277, -0.08430028990859442, -0.057566203857493364, -0.0872694188423233, 0.014087673449124143, -0.08378162358526442, -0.12736297316351966, 0.879034784646532, -0.09593062719660007, 0.9704531477293091, -0.18483373279423124, 0.9534490437632291, 0.1195751793040741, 0.17827265061365402, -0.012195462674915314, 0.8111348282621531, -0.14719422291089185, 0.09971024675427864, -0.12995465799148997, 0.04464843774220609, -0.12830810368943893, -0.02885104836952383, -0.05528716754923373, -0.017915161767790046, -0.10900606066954648, -0.07745576240225942, 0.8432078775722761, -0.149050476764493, 0.9341533707977246, 0.10287840988846135, 0.15328391785876944, 0.021679169185943144, 0.7199207312611301, -0.09831930104753049, 0.08382391893598595, -0.09267795261915576, -0.05763883280942824, -0.07234624929446642, -0.009584703278249144, -0.12165967388373586, -0.18629338352244168, 0.9260533181581867, 0.10994175814833423, 0.17235193978195115, -0.049131892253228136, 0.8007518165702124, -0.17390583808534502, -0.8054497778820967, -0.8107653310273767, -0.07735012879665097, -0.23825251410129192, 0.11892919689846587, 0.17564373819791718, 0.0046667156822598295, 0.7738771478249126, 0.9885256340313693, 0.10695484577926624, 0.1544572424295863, 0.10161180116201615, 0.2106834737579232, 0.05013581831921529]
朴素贝叶斯的训练集中连续型特征向量的属性有：
Index(['净资产收益率增长率B', '利息保障倍数B', '利润总额增长率B', '营业利润现金净含量', '净利润增长率B',
'所有者权益增长率B', '总资产增长率B', '经营杠杆', '固定资产增长率B', '存货与收入比', '财务杠杆',
'营业收入增长率B', '营业利润率', '综合杠杆', '速动比率', '现金及现金等价物周转率B', '现金比率', '有形净值债务率',
'流动比率', '资产负债率', '保守速动比率', '总资产净利润率（ROA）B', '营业收入现金净含量', '权益对负债比率'],
dtype='object', name=0)
各列相关系数：
[0.9155688145659241, 0.20421321586603097, 0.2293005602801019]
朴素贝叶斯的训练集中离散型特征向量的属性有：
Index(['股本总数', '监事总规模'], dtype='object', name=0)
In [870]:
from sklearn.naive_bayes import MultinomialNB # 处理财报中的离散变量。
from sklearn.naive_bayes import GaussianNB
import math
# 将上一步得到的线性模型使用的样本集也按照和之前划分训练集、测试集同样的方法进行划分。训练集：测试集=1：1
train_continuous_set = sample_continuous_bayes.iloc[::2,:] # 将去除多重共线性的样本转化为ndarray数据类型，用于朴素贝叶斯模型的训练。
test_continuous_set = sample_continuous_bayes.iloc[1:sample_continuous_bayes.shape[0]:2,:]
train_continuous_set.reset_index(drop=True, inplace=True)
test_continuous_set.reset_index(drop=True, inplace=True)
train_discrete_set = sample_discrete_bayes.iloc[::2,:]
test_discrete_set = sample_discrete_bayes.iloc[1:sample_discrete_bayes.shape[0]:2,:]
train_discrete_set.reset_index(drop=True, inplace=True)
test_discrete_set.reset_index(drop=True, inplace=True)
# y_train和y_test不变，沿用之前的变量。
X_train_continuous = np.array(train_continuous_set)
X_test_continuous = np.array(test_continuous_set)
X_train_discrete = np.array(train_discrete_set)
X_test_discrete = np.array(test_discrete_set)
# 生成一个长度为训练集样本容量大小的list，将负例样本的权重设为大于1的某个整数。这个sample_weight在连续型样本和离散型样本上通用。
# 权重为array-like，1 for unweighted samples。也可以None，表示全体样本无权重
sample_weight_value = 100
sample_weight_train = [sample_weight_value if train_set.loc[i, "label"]==-1 else 1
for i in range(train_continuous_set.shape[0])]
# 对连续型特征向量进行高斯朴素贝叶斯训练
clf_gauss = GaussianNB() # 初始化高斯朴素贝叶斯模型实例
clf_gauss.partial_fit(X_train_continuous, y_train, classes=[1,-1], sample_weight=sample_weight_train)
continuous_pred_prob = clf_gauss.predict_log_proba(X_test_continuous)
print("检查输出概率的shape:",continuous_pred_prob.shape)
# 对离散型特征变量进行多项式朴素贝叶斯训练
clf_multinomial = MultinomialNB() # 默认使用拉普拉斯平滑，alpha=1
clf_multinomial.partial_fit(X_train_discrete, y_train, classes=[1,-1], sample_weight=sample_weight_train)
discrete_pred_prob = clf_multinomial.predict_log_proba(X_test_discrete)
print("检查输出概率的shape:",discrete_pred_prob.shape)
# 然后将上一步得到的离散特征向量的输出概率和连续特征向量的输出概率相加，作为最终全部向量空间的输出概率。根据该输出概率判断样本属于哪个
# 分类。
y_pred_prob_bayes = continuous_pred_prob + discrete_pred_prob
print("最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:\n", y_pred_prob_bayes)
print("检查得到的对数概率shape中对应的分类标签：", clf_gauss.classes_, clf_multinomial.classes_)
y_pred_bayes = np.argmax(y_pred_prob_bayes, axis=1) # 沿着列方向比较大小，取最大值所在的index。
y_pred_bayes[y_pred_bayes==0] = -1
print("朴素贝叶斯预测的结果是：\n", y_pred_bayes)
print("朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：\n", np.where(y_pred_bayes==-1))
print("测试集中真实舞弊样本的index是：\n", y_test[y_test==-1])
print("测试集中预测为舞弊样本的个数有：", np.where(y_pred_bayes==-1)[0].size)
# 计算训练模型clf_op的AUC值，评估模型质量。
auc_values_bayes = auc(y_test, y_pred_bayes)
print("朴素贝叶斯模型得到的AUC值：", auc_values_bayes)
# 打印朴素贝叶斯的分类效果报告
print("用最优朴素贝叶斯模型，得到的分类结果评估报告如下：\n", classification_report(y_test, y_pred_bayes))
检查输出概率的shape: (1257, 2)
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-2.52057084e+01 -3.95261973e-01]
[-1.11949045e+00 -9.17282003e+00]
[-1.11941376e+00 -9.86085160e+00]
...
[-3.30315716e+01 -3.08064432e+00]
[-6.91381894e+02 -3.48773480e+00]
[-3.66792449e-01 -1.42372580e+01]]
检查得到的对数概率shape中对应的分类标签： [-1 1] [-1 1]
朴素贝叶斯预测的结果是：
[ 1 -1 -1 ... 1 1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 1, 2, 3, 4, 7, 8, 9, 10, 15, 16, 17,
18, 20, 22, 25, 26, 27, 29, 34, 42, 44, 45,
46, 47, 48, 50, 51, 52, 53, 57, 58, 59, 60,
63, 66, 67, 68, 69, 70, 76, 80, 81, 82, 83,
85, 86, 87, 88, 89, 90, 95, 98, 99, 100, 101,
102, 110, 117, 120, 121, 122, 127, 128, 129, 130, 131,
132, 134, 139, 140, 141, 142, 143, 144, 145, 146, 147,
148, 149, 151, 152, 154, 155, 156, 158, 164, 166, 167,
168, 169, 171, 174, 175, 176, 177, 179, 180, 181, 182,
184, 186, 188, 189, 193, 198, 201, 203, 205, 206, 207,
208, 210, 212, 217, 218, 219, 224, 227, 228, 229, 230,
231, 232, 233, 239, 240, 241, 242, 243, 244, 245, 246,
248, 249, 250, 251, 252, 253, 254, 255, 271, 273, 274,
275, 281, 282, 283, 284, 285, 286, 287, 289, 290, 291,
292, 293, 294, 295, 297, 302, 303, 305, 306, 309, 310,
311, 312, 313, 314, 315, 316, 327, 328, 329, 331, 332,
333, 334, 335, 336, 337, 338, 339, 341, 342, 343, 344,
345, 346, 347, 348, 349, 351, 352, 353, 354, 355, 357,
360, 361, 362, 363, 364, 365, 366, 368, 370, 376, 377,
378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388,
389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399,
400, 401, 402, 405, 406, 407, 408, 409, 415, 419, 420,
421, 422, 423, 424, 425, 426, 427, 428, 434, 435, 436,
437, 438, 439, 445, 446, 449, 450, 451, 452, 453, 457,
458, 459, 462, 469, 471, 472, 473, 475, 476, 477, 479,
481, 482, 483, 489, 490, 491, 492, 493, 495, 496, 497,
498, 499, 500, 501, 502, 505, 506, 507, 508, 509, 510,
511, 512, 514, 517, 518, 519, 520, 521, 522, 523, 524,
525, 527, 528, 530, 531, 532, 533, 535, 537, 538, 539,
540, 544, 545, 546, 553, 554, 555, 557, 560, 561, 562,
573, 574, 575, 576, 577, 579, 580, 581, 584, 586, 591,
592, 593, 594, 595, 597, 598, 599, 600, 601, 604, 605,
606, 609, 611, 616, 619, 620, 621, 622, 623, 625, 626,
633, 640, 643, 649, 651, 652, 653, 654, 658, 659, 662,
663, 668, 669, 670, 671, 672, 673, 676, 677, 679, 681,
682, 683, 684, 686, 687, 688, 690, 691, 693, 694, 696,
697, 698, 699, 700, 702, 703, 704, 705, 706, 707, 708,
709, 710, 711, 712, 713, 714, 715, 718, 719, 720, 721,
722, 723, 724, 725, 726, 727, 729, 730, 732, 733, 734,
735, 737, 739, 740, 742, 744, 746, 747, 748, 750, 751,
752, 753, 754, 755, 757, 758, 759, 760, 761, 762, 763,
764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774,
776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 788,
789, 791, 792, 793, 794, 796, 804, 805, 806, 807, 808,
809, 810, 811, 813, 814, 815, 817, 818, 819, 824, 825,
826, 827, 828, 829, 831, 836, 837, 838, 842, 843, 850,
851, 852, 853, 854, 860, 861, 862, 864, 865, 866, 867,
868, 872, 873, 875, 877, 889, 890, 891, 893, 894, 895,
897, 898, 899, 902, 903, 905, 907, 915, 916, 917, 918,
919, 925, 926, 927, 928, 929, 930, 938, 939, 941, 942,
943, 944, 952, 958, 959, 962, 963, 970, 971, 974, 975,
983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993,
995, 997, 1010, 1011, 1012, 1013, 1014, 1016, 1017, 1018, 1019,
1023, 1024, 1025, 1043, 1044, 1045, 1049, 1050, 1051, 1052, 1053,
1054, 1055, 1066, 1068, 1069, 1070, 1075, 1076, 1077, 1080, 1081,
1104, 1105, 1106, 1107, 1110, 1111, 1112, 1113, 1115, 1116, 1118,
1119, 1120, 1122, 1123, 1124, 1125, 1134, 1136, 1137, 1138, 1139,
1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1150, 1152, 1153,
1155, 1157, 1158, 1161, 1162, 1163, 1165, 1168, 1171, 1172, 1173,
1174, 1177, 1178, 1180, 1181, 1182, 1184, 1185, 1186, 1189, 1190,
1191, 1193, 1201, 1202, 1203, 1204, 1205, 1206, 1208, 1209, 1215,
1216, 1217, 1218, 1219, 1221, 1222, 1223, 1224, 1225, 1226, 1228,
1229, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244,
1245, 1246, 1247, 1248, 1251, 1252, 1253, 1256], dtype=int64),)
测试集中真实舞弊样本的index是：
[-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1]
测试集中预测为舞弊样本的个数有： 701
朴素贝叶斯模型得到的AUC值： 0.5396906464322195
用最优朴素贝叶斯模型，得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.01 0.64 0.02 11
1 0.99 0.44 0.61 1246
avg / total 0.98 0.44 0.61 1257
In [876]:
# 对上述结果进一步考虑，如果将sample_bayes样本集中的特征，逐一减掉fishvalue最小的特征，这样遍历不同数目特征的样本集，分类性能是否会有
# 改善？
auc_bayes_iter_contin = []
for i in range(X_train_continuous.shape[1]):
print("对连续型特征向量第%d轮迭代。"%(i+1))
X_train_contin_iter = X_train_continuous[:,i:]
X_test_contin_iter = X_test_continuous[:,i:]
clf_gauss_iter = GaussianNB() # 初始化高斯朴素贝叶斯模型实例
clf_gauss_iter.partial_fit(X_train_contin_iter, y_train, classes=[1,-1], sample_weight=sample_weight_train)
contin_pred_prob_iter = clf_gauss_iter.predict_log_proba(X_test_contin_iter)
print("检查输出概率的shape:",contin_pred_prob_iter.shape)
y_pred_prob_iter_for_contin = contin_pred_prob_iter + discrete_pred_prob # discrete_pred_prob是之前未迭代时计算的离散特征向量的预测对数概率。
print("最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:\n", y_pred_prob_iter_for_contin)
print("检查得到的对数概率shape中对应的分类标签：", clf_gauss_iter.classes_)
y_pred_bayes_iter_for_contin = np.argmax(y_pred_prob_iter_for_contin, axis=1) # 沿着列方向比较大小，取最大值所在的index。
y_pred_bayes_iter_for_contin[y_pred_bayes_iter_for_contin==0] = -1
print("朴素贝叶斯预测的结果是：\n", y_pred_bayes_iter_for_contin)
print("朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：\n", np.where(y_pred_bayes_iter_for_contin==-1))
print("测试集中预测为舞弊样本的个数有：", np.where(y_pred_bayes_iter_for_contin==-1)[0].size)
# 计算训练模型clf_op的AUC值，评估模型质量。
auc_value_op_bayes_for_contin = auc(y_test, y_pred_bayes_iter_for_contin)
print("朴素贝叶斯模型得到的AUC值：", auc_value_op_bayes_for_contin)
auc_bayes_iter_contin.append(auc_value_op_bayes_for_contin)
feature_count_forbayes_for_contin = [X_train_continuous.shape[1]-i for i in range(X_train_continuous.shape[1])]
print("连续型特征的样本特征数目：", feature_count_forbayes_for_contin)
print("朴素贝叶斯模型得到的AUC值分别是：\n",auc_bayes_iter_contin)
auc_bayes_iter_discrete = []
for i in range(X_train_discrete.shape[1]):
print("对离散型特征向量第%d轮迭代。"%(i+1))
X_train_discrete_iter = X_train_discrete[:,i:]
X_test_discrete_iter = X_test_discrete[:,i:]
clf_multinomial_iter = MultinomialNB() # 默认使用拉普拉斯平滑，alpha=1
clf_multinomial_iter.partial_fit(X_train_discrete_iter, y_train, classes=[1,-1], sample_weight=sample_weight_train)
discrete_pred_prob_iter = clf_multinomial_iter.predict_log_proba(X_test_discrete_iter)
y_pred_prob_iter_for_discrete = continuous_pred_prob + discrete_pred_prob_iter
y_pred_bayes_iter_for_discrete = np.argmax(y_pred_prob_iter_for_discrete, axis=1)
y_pred_bayes_iter_for_discrete[y_pred_bayes_iter_for_discrete==0] = -1
print("朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：\n", np.where(y_pred_bayes_iter_for_discrete==-1))
print("测试集中预测为舞弊样本的个数有：", np.where(y_pred_bayes_iter_for_discrete==-1)[0].size)
# 计算训练模型clf_op的AUC值，评估模型质量。
auc_value_op_bayes_for_discrete = auc(y_test, y_pred_bayes_iter_for_discrete)
print("朴素贝叶斯模型得到的AUC值：", auc_value_op_bayes_for_discrete)
auc_bayes_iter_discrete.append(auc_value_op_bayes_for_discrete)
feature_count_forbayes_for_discrete = [X_train_discrete.shape[1]-i for i in range(X_train_discrete.shape[1])]
print("离散型特征的样本特征数目：", feature_count_forbayes_for_discrete)
print("朴素贝叶斯模型得到的AUC值分别是：\n",auc_bayes_iter_discrete)
# 朴素贝叶斯不同特征数目的AUC值的变化。综合看，从2个特征到19个特征，AUC值变化不是很大。为尽可能保留特征，因此选择18个特征训练Naive Bayes
# model。
auc_plot_by_feature_selection(feature_count_forbayes_for_contin, auc_bayes_iter_contin)
auc_plot_by_feature_selection(feature_count_forbayes_for_discrete, auc_bayes_iter_discrete)
对连续型特征向量第1轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-2.52057084e+01 -3.95261973e-01]
[-1.11949045e+00 -9.17282003e+00]
[-1.11941376e+00 -9.86085160e+00]
...
[-3.30315716e+01 -3.08064432e+00]
[-6.91381894e+02 -3.48773480e+00]
[-3.66792449e-01 -1.42372580e+01]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 -1 -1 ... 1 1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 1, 2, 3, 4, 7, 8, 9, 10, 15, 16, 17,
18, 20, 22, 25, 26, 27, 29, 34, 42, 44, 45,
46, 47, 48, 50, 51, 52, 53, 57, 58, 59, 60,
63, 66, 67, 68, 69, 70, 76, 80, 81, 82, 83,
85, 86, 87, 88, 89, 90, 95, 98, 99, 100, 101,
102, 110, 117, 120, 121, 122, 127, 128, 129, 130, 131,
132, 134, 139, 140, 141, 142, 143, 144, 145, 146, 147,
148, 149, 151, 152, 154, 155, 156, 158, 164, 166, 167,
168, 169, 171, 174, 175, 176, 177, 179, 180, 181, 182,
184, 186, 188, 189, 193, 198, 201, 203, 205, 206, 207,
208, 210, 212, 217, 218, 219, 224, 227, 228, 229, 230,
231, 232, 233, 239, 240, 241, 242, 243, 244, 245, 246,
248, 249, 250, 251, 252, 253, 254, 255, 271, 273, 274,
275, 281, 282, 283, 284, 285, 286, 287, 289, 290, 291,
292, 293, 294, 295, 297, 302, 303, 305, 306, 309, 310,
311, 312, 313, 314, 315, 316, 327, 328, 329, 331, 332,
333, 334, 335, 336, 337, 338, 339, 341, 342, 343, 344,
345, 346, 347, 348, 349, 351, 352, 353, 354, 355, 357,
360, 361, 362, 363, 364, 365, 366, 368, 370, 376, 377,
378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388,
389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399,
400, 401, 402, 405, 406, 407, 408, 409, 415, 419, 420,
421, 422, 423, 424, 425, 426, 427, 428, 434, 435, 436,
437, 438, 439, 445, 446, 449, 450, 451, 452, 453, 457,
458, 459, 462, 469, 471, 472, 473, 475, 476, 477, 479,
481, 482, 483, 489, 490, 491, 492, 493, 495, 496, 497,
498, 499, 500, 501, 502, 505, 506, 507, 508, 509, 510,
511, 512, 514, 517, 518, 519, 520, 521, 522, 523, 524,
525, 527, 528, 530, 531, 532, 533, 535, 537, 538, 539,
540, 544, 545, 546, 553, 554, 555, 557, 560, 561, 562,
573, 574, 575, 576, 577, 579, 580, 581, 584, 586, 591,
592, 593, 594, 595, 597, 598, 599, 600, 601, 604, 605,
606, 609, 611, 616, 619, 620, 621, 622, 623, 625, 626,
633, 640, 643, 649, 651, 652, 653, 654, 658, 659, 662,
663, 668, 669, 670, 671, 672, 673, 676, 677, 679, 681,
682, 683, 684, 686, 687, 688, 690, 691, 693, 694, 696,
697, 698, 699, 700, 702, 703, 704, 705, 706, 707, 708,
709, 710, 711, 712, 713, 714, 715, 718, 719, 720, 721,
722, 723, 724, 725, 726, 727, 729, 730, 732, 733, 734,
735, 737, 739, 740, 742, 744, 746, 747, 748, 750, 751,
752, 753, 754, 755, 757, 758, 759, 760, 761, 762, 763,
764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774,
776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 788,
789, 791, 792, 793, 794, 796, 804, 805, 806, 807, 808,
809, 810, 811, 813, 814, 815, 817, 818, 819, 824, 825,
826, 827, 828, 829, 831, 836, 837, 838, 842, 843, 850,
851, 852, 853, 854, 860, 861, 862, 864, 865, 866, 867,
868, 872, 873, 875, 877, 889, 890, 891, 893, 894, 895,
897, 898, 899, 902, 903, 905, 907, 915, 916, 917, 918,
919, 925, 926, 927, 928, 929, 930, 938, 939, 941, 942,
943, 944, 952, 958, 959, 962, 963, 970, 971, 974, 975,
983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993,
995, 997, 1010, 1011, 1012, 1013, 1014, 1016, 1017, 1018, 1019,
1023, 1024, 1025, 1043, 1044, 1045, 1049, 1050, 1051, 1052, 1053,
1054, 1055, 1066, 1068, 1069, 1070, 1075, 1076, 1077, 1080, 1081,
1104, 1105, 1106, 1107, 1110, 1111, 1112, 1113, 1115, 1116, 1118,
1119, 1120, 1122, 1123, 1124, 1125, 1134, 1136, 1137, 1138, 1139,
1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1150, 1152, 1153,
1155, 1157, 1158, 1161, 1162, 1163, 1165, 1168, 1171, 1172, 1173,
1174, 1177, 1178, 1180, 1181, 1182, 1184, 1185, 1186, 1189, 1190,
1191, 1193, 1201, 1202, 1203, 1204, 1205, 1206, 1208, 1209, 1215,
1216, 1217, 1218, 1219, 1221, 1222, 1223, 1224, 1225, 1226, 1228,
1229, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244,
1245, 1246, 1247, 1248, 1251, 1252, 1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 701
朴素贝叶斯模型得到的AUC值： 0.5396906464322195
对连续型特征向量第2轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-2.50241735e+01 -3.95261973e-01]
[-1.11946642e+00 -9.34225857e+00]
[-1.11940129e+00 -1.00363778e+01]
...
[-3.28691256e+01 -3.08064432e+00]
[-6.91154409e+02 -3.48773480e+00]
[-3.66792049e-01 -1.44447337e+01]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 -1 -1 ... 1 1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 1, 2, 3, 4, 7, 8, 9, 10, 15, 16, 17,
18, 20, 22, 25, 26, 27, 29, 34, 42, 44, 45,
46, 47, 48, 50, 51, 52, 53, 57, 58, 59, 60,
63, 66, 67, 68, 69, 70, 76, 80, 81, 82, 83,
85, 86, 87, 88, 89, 90, 95, 98, 99, 100, 101,
102, 110, 117, 120, 121, 122, 127, 128, 129, 130, 131,
132, 134, 139, 140, 141, 142, 143, 144, 145, 146, 147,
148, 149, 151, 152, 154, 155, 156, 158, 164, 166, 167,
168, 169, 171, 174, 175, 176, 177, 179, 180, 181, 182,
184, 186, 188, 189, 193, 198, 201, 203, 205, 206, 207,
208, 210, 212, 217, 218, 219, 224, 227, 228, 229, 230,
231, 232, 233, 239, 240, 241, 242, 243, 244, 245, 246,
248, 249, 250, 251, 252, 253, 254, 255, 271, 273, 274,
275, 281, 282, 283, 284, 285, 286, 287, 289, 290, 291,
292, 293, 294, 295, 297, 302, 303, 305, 306, 309, 310,
311, 312, 313, 314, 315, 316, 327, 328, 329, 331, 332,
333, 334, 335, 336, 337, 338, 339, 341, 342, 343, 344,
345, 346, 347, 348, 349, 351, 352, 353, 354, 355, 357,
360, 361, 362, 363, 364, 365, 366, 368, 370, 376, 377,
378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388,
389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399,
400, 401, 402, 405, 406, 407, 408, 409, 415, 419, 420,
421, 422, 423, 424, 425, 426, 427, 428, 434, 435, 436,
437, 438, 439, 445, 446, 449, 450, 451, 452, 453, 457,
458, 459, 462, 469, 471, 472, 473, 475, 476, 477, 479,
481, 482, 483, 489, 490, 491, 492, 493, 495, 496, 497,
498, 499, 500, 501, 502, 505, 506, 507, 508, 509, 510,
511, 512, 514, 517, 518, 519, 520, 521, 522, 523, 524,
525, 527, 528, 530, 531, 532, 533, 535, 537, 538, 539,
540, 544, 545, 546, 553, 554, 555, 557, 560, 561, 562,
573, 574, 575, 576, 577, 579, 580, 581, 584, 586, 591,
592, 593, 594, 595, 597, 598, 599, 600, 601, 604, 605,
606, 609, 611, 616, 619, 620, 621, 622, 623, 625, 626,
633, 640, 643, 649, 651, 652, 653, 654, 658, 659, 662,
663, 668, 669, 670, 671, 672, 673, 676, 677, 679, 681,
682, 683, 684, 686, 687, 688, 690, 691, 693, 694, 696,
697, 698, 699, 700, 702, 703, 704, 705, 706, 707, 708,
709, 710, 711, 712, 713, 714, 715, 718, 719, 720, 721,
722, 723, 724, 725, 726, 727, 729, 730, 732, 733, 734,
735, 737, 739, 740, 742, 744, 746, 747, 748, 750, 751,
752, 753, 754, 755, 757, 758, 759, 760, 761, 762, 763,
764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774,
776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 788,
789, 791, 792, 793, 794, 796, 804, 805, 806, 807, 808,
809, 810, 811, 813, 814, 815, 817, 818, 819, 824, 825,
826, 827, 828, 829, 831, 836, 837, 838, 842, 843, 850,
851, 852, 853, 854, 860, 861, 862, 864, 865, 866, 867,
868, 872, 873, 875, 877, 889, 890, 891, 893, 894, 895,
897, 898, 899, 902, 903, 904, 905, 907, 915, 916, 917,
918, 919, 925, 926, 927, 928, 929, 930, 938, 939, 941,
942, 943, 944, 952, 958, 959, 962, 963, 970, 971, 974,
975, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992,
993, 995, 997, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017,
1018, 1019, 1023, 1024, 1025, 1043, 1044, 1045, 1049, 1050, 1051,
1052, 1053, 1054, 1055, 1066, 1068, 1069, 1070, 1075, 1076, 1077,
1080, 1081, 1104, 1105, 1106, 1107, 1110, 1111, 1112, 1113, 1115,
1116, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1134, 1136,
1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147,
1150, 1152, 1153, 1155, 1157, 1158, 1161, 1162, 1163, 1165, 1168,
1171, 1172, 1173, 1174, 1177, 1178, 1180, 1181, 1182, 1184, 1185,
1186, 1189, 1190, 1191, 1193, 1201, 1202, 1203, 1204, 1205, 1206,
1208, 1209, 1215, 1216, 1217, 1218, 1219, 1221, 1222, 1223, 1224,
1225, 1226, 1228, 1229, 1235, 1236, 1237, 1238, 1239, 1240, 1241,
1242, 1243, 1244, 1245, 1246, 1247, 1248, 1251, 1252, 1253, 1256],
dtype=int64),)
测试集中预测为舞弊样本的个数有： 704
朴素贝叶斯模型得到的AUC值： 0.5384867941047716
对连续型特征向量第3轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[ -32.11788893 -0.39526197]
[ -1.13729791 -4.423747 ]
[ -1.12807971 -5.13908341]
...
[ -77.6148932 -3.08064432]
[-333.01166047 -3.4877348 ]
[ -0.36679189 -14.54274151]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 -1 -1 ... 1 1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 1, 2, 3, 4, 6, 7, 8, 9, 15, 16, 17,
18, 25, 26, 27, 28, 29, 34, 45, 46, 48, 50,
51, 52, 55, 59, 60, 68, 69, 70, 80, 81, 82,
83, 85, 86, 87, 88, 89, 98, 99, 100, 101, 102,
117, 121, 127, 129, 130, 131, 132, 134, 139, 140, 142,
143, 144, 145, 146, 147, 148, 149, 151, 152, 154, 155,
156, 164, 167, 168, 169, 170, 171, 174, 175, 176, 177,
180, 181, 186, 188, 189, 193, 198, 203, 205, 206, 207,
217, 218, 219, 224, 227, 228, 239, 241, 243, 244, 245,
248, 249, 250, 251, 252, 253, 254, 255, 271, 273, 274,
275, 281, 282, 283, 285, 286, 287, 289, 290, 291, 292,
293, 294, 297, 302, 303, 305, 309, 310, 311, 312, 313,
314, 315, 326, 327, 329, 332, 333, 334, 336, 337, 338,
339, 341, 342, 343, 344, 345, 347, 348, 349, 350, 351,
352, 353, 354, 355, 357, 360, 361, 362, 366, 368, 370,
376, 377, 378, 379, 380, 381, 382, 383, 384, 386, 387,
388, 389, 390, 391, 392, 393, 394, 396, 397, 398, 399,
400, 401, 402, 405, 406, 407, 408, 409, 415, 419, 420,
421, 422, 423, 424, 425, 426, 427, 428, 434, 435, 436,
437, 438, 445, 446, 449, 450, 451, 452, 453, 458, 459,
460, 462, 469, 471, 472, 473, 475, 476, 477, 479, 480,
482, 483, 489, 490, 491, 492, 493, 495, 496, 497, 498,
499, 500, 501, 502, 505, 506, 507, 508, 510, 511, 512,
518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528,
530, 531, 533, 535, 539, 540, 545, 546, 555, 560, 561,
562, 574, 575, 576, 577, 579, 580, 584, 591, 592, 593,
594, 597, 598, 600, 604, 605, 606, 609, 611, 619, 621,
622, 626, 637, 640, 642, 643, 649, 651, 652, 653, 654,
658, 668, 669, 670, 671, 672, 673, 676, 677, 678, 679,
681, 682, 683, 684, 685, 686, 687, 690, 691, 693, 694,
696, 697, 698, 700, 702, 703, 704, 705, 706, 707, 712,
714, 715, 718, 719, 720, 721, 722, 723, 724, 725, 726,
727, 729, 730, 731, 732, 733, 734, 735, 739, 740, 741,
744, 745, 746, 747, 748, 751, 752, 753, 754, 755, 757,
758, 759, 762, 764, 765, 766, 767, 768, 769, 770, 771,
772, 773, 774, 776, 777, 778, 779, 780, 781, 782, 783,
784, 785, 791, 792, 793, 796, 805, 808, 809, 810, 811,
813, 814, 815, 819, 824, 825, 827, 828, 829, 831, 842,
843, 850, 851, 852, 853, 854, 861, 864, 865, 866, 867,
868, 873, 889, 890, 891, 893, 894, 899, 903, 907, 915,
916, 917, 918, 919, 928, 930, 936, 938, 939, 942, 943,
944, 952, 973, 974, 984, 985, 986, 987, 988, 989, 990,
991, 992, 993, 995, 997, 1010, 1011, 1012, 1013, 1014, 1019,
1020, 1024, 1025, 1044, 1045, 1049, 1050, 1051, 1052, 1053, 1054,
1066, 1069, 1070, 1080, 1110, 1111, 1112, 1113, 1116, 1118, 1119,
1120, 1122, 1123, 1124, 1125, 1134, 1136, 1137, 1138, 1139, 1141,
1142, 1143, 1144, 1145, 1146, 1147, 1149, 1150, 1152, 1153, 1155,
1161, 1162, 1163, 1171, 1172, 1173, 1178, 1180, 1181, 1182, 1185,
1189, 1190, 1193, 1195, 1198, 1201, 1202, 1203, 1205, 1206, 1209,
1215, 1216, 1217, 1218, 1219, 1222, 1223, 1224, 1228, 1229, 1235,
1236, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248,
1251, 1252, 1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 565
朴素贝叶斯模型得到的AUC值： 0.5942652852765212
对连续型特征向量第4轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[ -32.53252167 -0.39526197]
[ -1.14693755 -3.99892457]
[ -1.13318068 -4.68205118]
...
[ -78.14731457 -3.08064432]
[-329.41794807 -3.4877348 ]
[ -0.36679321 -13.93396468]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 -1 -1 ... 1 1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 1, 2, 3, 4, 6, 7, 8, 9, 15, 16, 17,
18, 25, 26, 27, 28, 29, 34, 45, 46, 48, 50,
51, 52, 55, 59, 60, 68, 69, 70, 80, 81, 82,
83, 85, 86, 87, 88, 89, 98, 99, 100, 101, 102,
117, 121, 127, 128, 129, 130, 131, 132, 134, 139, 140,
142, 143, 144, 145, 146, 148, 149, 151, 152, 154, 155,
156, 164, 167, 168, 169, 170, 171, 174, 175, 176, 177,
180, 181, 186, 188, 189, 193, 198, 203, 205, 206, 207,
217, 218, 219, 224, 227, 239, 241, 243, 244, 245, 248,
249, 250, 251, 252, 253, 254, 255, 271, 273, 274, 275,
281, 282, 283, 285, 286, 287, 288, 289, 290, 291, 292,
293, 294, 297, 302, 303, 305, 309, 310, 311, 312, 313,
314, 315, 326, 327, 329, 332, 333, 334, 336, 337, 338,
339, 341, 342, 343, 344, 345, 347, 348, 349, 350, 351,
352, 353, 354, 355, 357, 360, 361, 362, 366, 368, 370,
376, 377, 378, 379, 380, 381, 382, 383, 384, 386, 387,
388, 389, 390, 392, 393, 394, 396, 397, 398, 399, 400,
401, 402, 405, 406, 407, 408, 409, 415, 419, 420, 421,
422, 423, 424, 425, 426, 427, 434, 435, 436, 437, 438,
445, 446, 448, 449, 450, 451, 452, 453, 458, 459, 460,
462, 469, 471, 472, 473, 475, 476, 477, 479, 480, 482,
483, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498,
499, 500, 501, 502, 505, 506, 507, 508, 510, 511, 512,
518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528,
530, 531, 533, 535, 539, 540, 545, 546, 555, 560, 561,
562, 574, 575, 576, 577, 579, 580, 584, 591, 592, 593,
594, 597, 598, 600, 604, 605, 606, 609, 611, 619, 621,
622, 626, 637, 640, 642, 643, 649, 651, 652, 653, 654,
658, 668, 669, 670, 671, 672, 673, 676, 677, 678, 679,
681, 682, 683, 684, 685, 686, 687, 690, 691, 693, 694,
696, 697, 698, 700, 702, 703, 704, 705, 706, 707, 712,
714, 715, 718, 719, 720, 721, 722, 723, 724, 725, 726,
727, 729, 730, 731, 732, 733, 734, 735, 740, 741, 744,
745, 746, 747, 748, 752, 753, 754, 755, 757, 758, 759,
762, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773,
774, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785,
791, 792, 796, 805, 808, 809, 810, 811, 814, 815, 819,
824, 825, 827, 828, 829, 831, 842, 843, 850, 851, 852,
853, 854, 861, 864, 865, 866, 867, 868, 889, 890, 891,
893, 894, 897, 899, 903, 907, 915, 916, 917, 918, 919,
928, 930, 936, 942, 943, 944, 952, 973, 974, 984, 985,
986, 987, 988, 989, 990, 991, 992, 993, 995, 997, 1010,
1011, 1012, 1013, 1014, 1019, 1020, 1024, 1025, 1044, 1045, 1050,
1051, 1052, 1053, 1054, 1066, 1069, 1070, 1080, 1110, 1112, 1113,
1116, 1118, 1119, 1120, 1122, 1123, 1124, 1125, 1134, 1136, 1137,
1138, 1139, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1149, 1150,
1152, 1153, 1161, 1162, 1163, 1172, 1173, 1178, 1180, 1181, 1182,
1185, 1189, 1190, 1193, 1195, 1198, 1201, 1202, 1203, 1205, 1206,
1209, 1215, 1216, 1217, 1218, 1219, 1222, 1223, 1224, 1228, 1229,
1235, 1236, 1239, 1240, 1241, 1242, 1243, 1244, 1246, 1247, 1248,
1251, 1252, 1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 554
朴素贝叶斯模型得到的AUC值： 0.5986794104771632
对连续型特征向量第5轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[ -34.74225915 -0.39526197]
[ -1.34552993 -1.99259171]
[ -1.23897033 -2.57780052]
...
[ -80.37060838 -3.08064432]
[-331.28446347 -3.4877348 ]
[ -0.36681652 -11.73014374]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 -1 -1 ... 1 1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 1, 2, 3, 6, 7, 8, 9, 15, 16, 17, 18,
26, 27, 28, 45, 46, 48, 50, 51, 52, 55, 67,
68, 69, 70, 80, 81, 82, 83, 85, 86, 87, 89,
98, 99, 100, 101, 102, 117, 127, 128, 129, 130, 131,
132, 139, 142, 143, 144, 146, 149, 151, 152, 154, 155,
156, 164, 167, 168, 169, 170, 171, 176, 177, 186, 188,
198, 203, 204, 205, 217, 218, 219, 224, 227, 241, 243,
244, 248, 249, 250, 251, 253, 254, 255, 271, 273, 274,
281, 282, 283, 286, 287, 288, 289, 290, 291, 292, 293,
294, 297, 302, 303, 309, 310, 311, 312, 313, 314, 315,
326, 327, 329, 332, 333, 337, 338, 339, 341, 342, 343,
344, 345, 348, 349, 351, 352, 353, 354, 355, 357, 360,
361, 362, 366, 368, 370, 376, 377, 378, 379, 380, 381,
382, 383, 384, 386, 387, 388, 389, 390, 392, 393, 394,
396, 397, 398, 399, 400, 401, 402, 405, 406, 407, 408,
409, 419, 420, 421, 422, 423, 424, 425, 434, 435, 436,
437, 445, 446, 448, 449, 450, 451, 452, 453, 458, 459,
460, 462, 469, 471, 472, 473, 475, 477, 482, 483, 489,
490, 491, 493, 494, 495, 496, 497, 498, 499, 501, 502,
505, 506, 507, 510, 511, 512, 518, 519, 520, 521, 522,
523, 524, 525, 526, 527, 528, 530, 531, 533, 539, 540,
545, 555, 560, 561, 562, 574, 575, 576, 577, 579, 591,
592, 593, 594, 597, 598, 604, 605, 609, 611, 619, 621,
622, 626, 637, 640, 641, 642, 643, 649, 652, 653, 656,
658, 668, 669, 670, 672, 673, 676, 677, 678, 679, 681,
682, 683, 685, 686, 687, 690, 691, 693, 694, 696, 697,
698, 699, 700, 703, 704, 705, 706, 712, 714, 715, 718,
719, 720, 721, 722, 723, 725, 727, 730, 731, 732, 733,
734, 735, 740, 741, 744, 745, 746, 747, 748, 752, 753,
754, 755, 757, 758, 759, 762, 764, 766, 767, 768, 769,
770, 771, 772, 773, 776, 777, 778, 779, 780, 781, 782,
784, 785, 791, 792, 796, 805, 808, 809, 810, 811, 819,
824, 825, 827, 828, 831, 842, 843, 850, 852, 853, 854,
861, 864, 865, 866, 867, 889, 894, 899, 903, 907, 916,
917, 918, 919, 936, 943, 952, 973, 974, 984, 985, 986,
987, 988, 989, 990, 992, 993, 997, 1010, 1011, 1012, 1013,
1014, 1019, 1020, 1025, 1044, 1045, 1050, 1051, 1052, 1053, 1054,
1066, 1069, 1070, 1080, 1112, 1113, 1116, 1118, 1119, 1120, 1123,
1124, 1125, 1134, 1136, 1137, 1138, 1139, 1141, 1142, 1143, 1145,
1146, 1147, 1149, 1150, 1152, 1161, 1162, 1163, 1172, 1173, 1178,
1180, 1181, 1182, 1190, 1193, 1195, 1198, 1202, 1203, 1206, 1209,
1215, 1216, 1217, 1218, 1219, 1222, 1223, 1224, 1229, 1235, 1236,
1239, 1240, 1241, 1242, 1243, 1244, 1246, 1247, 1248, 1251, 1252,
1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 475
朴素贝叶斯模型得到的AUC值： 0.6303808550999561
对连续型特征向量第6轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[ -34.11084267 -0.39526197]
[ -1.24707072 -2.5162513 ]
[ -1.18591386 -3.13775355]
...
[ -79.77774136 -3.08064432]
[-331.19129789 -3.4877348 ]
[ -0.36680555 -12.27234647]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 -1 -1 ... 1 1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 1, 2, 3, 4, 6, 7, 8, 9, 15, 16, 17,
18, 26, 27, 28, 29, 34, 45, 46, 48, 50, 51,
52, 55, 67, 68, 69, 70, 80, 81, 82, 83, 85,
86, 87, 89, 98, 99, 100, 101, 102, 117, 127, 128,
129, 130, 131, 132, 134, 139, 142, 143, 144, 146, 149,
151, 152, 154, 155, 156, 164, 167, 168, 169, 170, 171,
174, 176, 177, 180, 186, 188, 189, 198, 203, 204, 205,
207, 217, 218, 219, 224, 227, 239, 241, 243, 244, 248,
249, 250, 251, 253, 254, 255, 271, 273, 274, 275, 281,
282, 283, 286, 287, 288, 289, 290, 291, 292, 293, 294,
297, 302, 303, 309, 310, 311, 312, 313, 314, 315, 326,
327, 329, 332, 333, 337, 338, 339, 341, 342, 343, 344,
345, 348, 349, 350, 351, 352, 353, 354, 355, 357, 360,
361, 362, 366, 368, 370, 376, 377, 378, 379, 380, 381,
382, 383, 384, 386, 387, 388, 389, 390, 392, 393, 394,
396, 397, 398, 399, 400, 401, 402, 405, 406, 407, 408,
409, 419, 420, 421, 422, 423, 424, 425, 426, 434, 435,
436, 437, 438, 445, 446, 448, 449, 450, 451, 452, 453,
458, 459, 460, 462, 469, 471, 472, 473, 475, 477, 479,
482, 483, 489, 490, 491, 493, 495, 496, 497, 498, 499,
500, 501, 502, 505, 506, 507, 510, 511, 512, 518, 519,
520, 521, 523, 524, 525, 526, 527, 528, 530, 531, 533,
539, 540, 545, 555, 560, 561, 562, 574, 575, 576, 577,
579, 580, 584, 591, 592, 593, 594, 597, 598, 604, 605,
606, 609, 611, 619, 621, 622, 626, 637, 640, 641, 642,
643, 649, 652, 653, 656, 658, 668, 669, 670, 672, 673,
676, 677, 678, 679, 681, 682, 683, 685, 686, 687, 690,
691, 693, 694, 696, 697, 698, 699, 700, 703, 704, 705,
706, 707, 712, 714, 715, 718, 719, 720, 721, 722, 723,
725, 727, 730, 731, 732, 733, 734, 735, 740, 741, 744,
745, 746, 747, 748, 752, 753, 754, 755, 757, 758, 759,
762, 764, 766, 767, 768, 769, 770, 771, 772, 773, 776,
777, 778, 779, 780, 781, 782, 783, 784, 785, 791, 792,
796, 805, 808, 809, 810, 811, 819, 824, 825, 827, 828,
831, 842, 843, 850, 852, 853, 854, 861, 864, 865, 866,
867, 889, 890, 891, 893, 894, 899, 903, 907, 916, 917,
918, 919, 936, 943, 952, 973, 974, 984, 985, 986, 987,
988, 989, 990, 992, 997, 1010, 1011, 1012, 1013, 1014, 1019,
1020, 1025, 1044, 1045, 1050, 1051, 1052, 1053, 1054, 1066, 1069,
1070, 1080, 1112, 1113, 1116, 1118, 1119, 1120, 1123, 1124, 1125,
1134, 1136, 1137, 1138, 1139, 1141, 1142, 1143, 1145, 1146, 1147,
1149, 1150, 1152, 1161, 1162, 1163, 1172, 1173, 1178, 1180, 1181,
1182, 1190, 1193, 1195, 1198, 1201, 1202, 1203, 1206, 1209, 1215,
1216, 1217, 1218, 1219, 1222, 1223, 1224, 1229, 1235, 1236, 1239,
1240, 1241, 1242, 1243, 1244, 1246, 1247, 1248, 1251, 1252, 1253,
1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 496
朴素贝叶斯模型得到的AUC值： 0.6219538888078213
对连续型特征向量第7轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[ -34.51884645 -0.39526197]
[ -1.33504138 -2.03501967]
[ -1.23317056 -2.62465087]
...
[ -80.43315891 -3.08064432]
[-320.26348736 -3.4877348 ]
[ -0.36680402 -12.37843852]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 -1 -1 ... 1 1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 1, 2, 3, 6, 7, 8, 9, 15, 16, 17, 18,
26, 27, 28, 34, 45, 46, 48, 50, 51, 52, 55,
67, 68, 69, 70, 80, 81, 82, 83, 85, 86, 87,
89, 98, 99, 100, 101, 102, 117, 127, 128, 129, 130,
131, 132, 139, 142, 143, 144, 146, 149, 151, 152, 154,
155, 156, 164, 167, 168, 169, 170, 171, 176, 177, 186,
188, 198, 203, 204, 205, 217, 218, 219, 224, 227, 241,
243, 244, 248, 249, 250, 251, 253, 254, 255, 271, 273,
274, 281, 282, 283, 286, 287, 288, 289, 290, 291, 292,
293, 294, 297, 302, 303, 309, 310, 311, 312, 313, 314,
315, 326, 327, 329, 332, 333, 337, 338, 339, 341, 342,
343, 344, 345, 348, 349, 351, 352, 353, 354, 355, 357,
360, 361, 362, 366, 368, 370, 376, 377, 378, 379, 380,
381, 382, 383, 384, 386, 387, 388, 389, 390, 392, 393,
394, 396, 397, 398, 399, 400, 401, 402, 405, 406, 407,
408, 409, 419, 420, 421, 422, 423, 424, 425, 434, 435,
436, 437, 445, 446, 448, 449, 450, 451, 452, 453, 458,
459, 460, 462, 469, 471, 472, 473, 475, 477, 482, 483,
489, 490, 491, 493, 495, 496, 497, 498, 499, 501, 502,
505, 506, 507, 510, 511, 512, 518, 519, 520, 521, 523,
524, 525, 526, 527, 528, 530, 531, 533, 540, 545, 555,
560, 561, 562, 574, 575, 576, 577, 579, 580, 585, 591,
592, 593, 594, 597, 598, 604, 605, 606, 609, 611, 619,
621, 622, 625, 626, 637, 640, 641, 642, 643, 649, 652,
653, 656, 658, 668, 669, 670, 672, 673, 676, 677, 678,
679, 681, 682, 683, 685, 686, 687, 690, 691, 693, 694,
696, 697, 698, 699, 700, 703, 704, 705, 706, 712, 714,
715, 718, 719, 720, 721, 722, 723, 725, 727, 730, 731,
732, 733, 734, 735, 740, 741, 744, 745, 746, 747, 748,
752, 753, 754, 755, 757, 758, 759, 762, 764, 766, 767,
768, 769, 770, 771, 772, 773, 776, 777, 778, 779, 780,
781, 782, 784, 785, 791, 792, 796, 805, 808, 809, 810,
811, 819, 824, 825, 827, 828, 831, 842, 843, 850, 852,
853, 854, 861, 864, 865, 866, 867, 889, 890, 894, 899,
903, 907, 916, 917, 918, 919, 936, 943, 952, 973, 974,
984, 985, 986, 987, 988, 989, 990, 992, 997, 1010, 1011,
1012, 1013, 1014, 1019, 1020, 1025, 1044, 1045, 1050, 1051, 1052,
1053, 1054, 1066, 1069, 1070, 1080, 1112, 1113, 1116, 1118, 1119,
1120, 1123, 1124, 1125, 1134, 1136, 1137, 1138, 1139, 1141, 1142,
1143, 1145, 1146, 1147, 1149, 1150, 1152, 1161, 1162, 1163, 1172,
1173, 1178, 1180, 1181, 1182, 1190, 1193, 1195, 1198, 1202, 1203,
1206, 1209, 1215, 1216, 1217, 1218, 1219, 1222, 1223, 1224, 1229,
1235, 1236, 1239, 1240, 1241, 1242, 1243, 1244, 1246, 1247, 1248,
1251, 1252, 1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 477
朴素贝叶斯模型得到的AUC值： 0.6295782868816576
对连续型特征向量第8轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[ -34.48428216 -0.39526197]
[ -2.10793471 -0.86063291]
[ -1.72593071 -1.18316926]
...
[ -82.15078347 -3.08064432]
[-317.19689958 -3.4877348 ]
[ -0.36688368 -10.45976163]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... 1 1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 6, 7, 8, 9, 15, 16, 17, 18, 26, 27, 28,
45, 48, 50, 51, 52, 55, 67, 68, 69, 80, 81,
82, 83, 85, 86, 89, 98, 99, 100, 101, 102, 117,
127, 128, 130, 131, 132, 139, 142, 143, 144, 149, 151,
152, 154, 164, 167, 168, 169, 170, 171, 176, 186, 188,
198, 203, 204, 205, 217, 218, 219, 241, 243, 244, 248,
249, 250, 251, 252, 253, 254, 255, 271, 273, 281, 286,
287, 288, 289, 290, 291, 292, 293, 294, 297, 302, 303,
309, 310, 311, 312, 314, 327, 333, 337, 339, 341, 343,
344, 345, 346, 348, 351, 352, 353, 355, 361, 362, 363,
366, 368, 370, 376, 377, 378, 379, 380, 381, 383, 386,
387, 388, 389, 390, 392, 396, 397, 398, 399, 400, 401,
402, 405, 407, 408, 409, 419, 420, 421, 422, 423, 424,
425, 434, 435, 436, 445, 448, 449, 450, 451, 452, 453,
458, 459, 460, 469, 471, 472, 473, 475, 477, 482, 490,
491, 493, 495, 496, 497, 498, 499, 501, 502, 505, 506,
507, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528,
530, 531, 533, 540, 545, 555, 560, 561, 562, 574, 575,
576, 577, 579, 585, 591, 592, 593, 594, 597, 598, 604,
605, 609, 611, 619, 625, 626, 637, 640, 641, 642, 643,
652, 656, 658, 668, 670, 672, 673, 676, 677, 678, 679,
681, 682, 683, 685, 686, 690, 691, 693, 694, 696, 697,
698, 700, 703, 704, 705, 706, 712, 713, 714, 715, 718,
719, 720, 721, 722, 723, 725, 727, 730, 731, 732, 733,
734, 735, 740, 741, 744, 745, 746, 748, 752, 753, 754,
755, 757, 758, 759, 762, 766, 767, 768, 769, 771, 772,
773, 776, 777, 779, 780, 781, 782, 791, 792, 805, 808,
809, 810, 811, 819, 827, 828, 842, 852, 853, 854, 864,
865, 894, 899, 903, 907, 916, 917, 918, 919, 936, 943,
973, 974, 984, 985, 986, 987, 988, 989, 990, 1010, 1011,
1012, 1013, 1014, 1019, 1020, 1025, 1044, 1045, 1051, 1052, 1054,
1066, 1080, 1112, 1116, 1118, 1119, 1120, 1123, 1124, 1125, 1134,
1136, 1137, 1138, 1139, 1141, 1142, 1145, 1146, 1149, 1150, 1152,
1161, 1172, 1173, 1178, 1180, 1181, 1182, 1190, 1193, 1195, 1198,
1202, 1203, 1206, 1209, 1215, 1216, 1217, 1218, 1219, 1222, 1223,
1224, 1235, 1236, 1239, 1240, 1241, 1242, 1243, 1244, 1246, 1247,
1248, 1251, 1252, 1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 401
朴素贝叶斯模型得到的AUC值： 0.6600758791770028
对连续型特征向量第9轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[ -36.4774543 -0.39526197]
[ -3.40979304 -0.50197921]
[ -3.02782032 -0.55578882]
...
[ -84.32559408 -3.08064432]
[-319.8803004 -3.4877348 ]
[ -0.36683516 -11.1930168 ]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... 1 1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 9, 14, 15, 16, 17, 18, 19, 22, 25, 34, 44,
48, 50, 51, 52, 60, 67, 68, 69, 80, 81, 82,
83, 85, 89, 98, 99, 100, 101, 102, 115, 117, 118,
119, 127, 129, 130, 131, 132, 139, 142, 154, 155, 156,
157, 164, 167, 168, 169, 170, 171, 176, 186, 188, 198,
203, 204, 217, 218, 219, 224, 227, 236, 243, 244, 248,
249, 250, 251, 252, 254, 255, 257, 271, 273, 281, 286,
290, 291, 292, 293, 297, 302, 309, 310, 311, 312, 314,
327, 330, 331, 332, 341, 343, 344, 345, 348, 351, 352,
353, 354, 355, 357, 361, 362, 366, 370, 376, 377, 378,
379, 380, 381, 386, 387, 388, 389, 390, 391, 392, 396,
397, 398, 399, 400, 401, 402, 403, 405, 407, 409, 419,
420, 422, 423, 435, 436, 443, 448, 449, 450, 451, 452,
453, 454, 457, 458, 459, 460, 469, 471, 472, 473, 475,
482, 490, 493, 495, 496, 497, 498, 499, 501, 502, 505,
506, 520, 521, 527, 528, 533, 540, 541, 545, 568, 574,
575, 576, 591, 592, 593, 594, 598, 604, 609, 611, 619,
633, 635, 637, 640, 641, 642, 643, 652, 656, 658, 668,
669, 670, 672, 673, 676, 677, 678, 679, 681, 682, 683,
684, 685, 690, 691, 693, 694, 696, 697, 698, 699, 700,
703, 704, 705, 706, 712, 714, 715, 718, 719, 720, 721,
722, 723, 725, 727, 731, 732, 733, 734, 735, 741, 744,
745, 746, 752, 753, 754, 755, 757, 758, 759, 760, 761,
766, 767, 768, 769, 770, 771, 773, 779, 780, 781, 782,
791, 792, 808, 809, 810, 811, 819, 827, 828, 843, 852,
853, 854, 864, 893, 906, 907, 919, 936, 943, 973, 974,
984, 985, 986, 987, 988, 989, 990, 1010, 1011, 1012, 1014,
1044, 1051, 1052, 1054, 1077, 1080, 1116, 1123, 1124, 1134, 1137,
1138, 1139, 1141, 1145, 1146, 1150, 1152, 1173, 1178, 1180, 1181,
1182, 1195, 1203, 1205, 1206, 1209, 1215, 1216, 1217, 1218, 1219,
1222, 1223, 1224, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242,
1243, 1244, 1246, 1247, 1248, 1251, 1252, 1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 350
朴素贝叶斯模型得到的AUC值： 0.6805413687436159
对连续型特征向量第10轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[ -31.50110097 -0.39526197]
[ -4.51774412 -0.42925986]
[ -4.08247476 -0.44830052]
...
[ -85.50678171 -3.08064432]
[-321.06235268 -3.4877348 ]
[ -0.3669344 -10.02594103]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... 1 1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 14, 15, 16, 17, 18, 22, 48, 50, 51, 67, 68,
80, 81, 82, 85, 89, 98, 99, 100, 101, 115, 117,
118, 119, 127, 130, 131, 142, 154, 155, 156, 157, 164,
167, 168, 169, 170, 171, 176, 186, 198, 204, 217, 218,
219, 224, 236, 243, 244, 248, 249, 250, 251, 254, 255,
271, 273, 281, 283, 290, 291, 292, 293, 297, 302, 309,
310, 311, 312, 314, 327, 330, 331, 332, 341, 342, 343,
344, 345, 348, 351, 352, 353, 354, 355, 361, 362, 366,
370, 376, 377, 378, 379, 381, 386, 387, 388, 389, 390,
391, 392, 396, 397, 398, 399, 400, 401, 405, 407, 409,
419, 420, 421, 422, 435, 443, 449, 450, 451, 452, 454,
457, 458, 459, 469, 471, 472, 473, 475, 482, 493, 495,
496, 497, 499, 501, 502, 504, 521, 528, 533, 540, 541,
545, 574, 575, 576, 591, 592, 593, 594, 604, 609, 611,
619, 633, 635, 637, 640, 641, 642, 643, 652, 656, 658,
668, 669, 670, 672, 673, 676, 677, 678, 679, 681, 682,
683, 685, 689, 690, 691, 693, 694, 696, 697, 699, 700,
703, 704, 705, 706, 712, 713, 714, 715, 718, 719, 720,
721, 722, 723, 725, 727, 731, 732, 734, 735, 744, 745,
746, 752, 753, 754, 755, 757, 758, 759, 761, 766, 767,
768, 769, 770, 771, 779, 780, 781, 782, 791, 792, 808,
809, 819, 827, 828, 893, 905, 906, 907, 936, 973, 974,
984, 985, 986, 987, 988, 989, 990, 996, 1010, 1011, 1012,
1013, 1014, 1077, 1080, 1116, 1124, 1129, 1134, 1138, 1139, 1141,
1145, 1146, 1150, 1152, 1164, 1178, 1181, 1203, 1205, 1206, 1215,
1217, 1218, 1222, 1223, 1224, 1235, 1236, 1237, 1238, 1239, 1240,
1241, 1242, 1243, 1246, 1247, 1248, 1251, 1252, 1253, 1256],
dtype=int64),)
测试集中预测为舞弊样本的个数有： 296
朴素贝叶斯模型得到的AUC值： 0.7022107106376769
对连续型特征向量第11轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-3.35311165e+01 -3.95261973e-01]
[-6.71118725e+00 -3.98997061e-01]
[-6.15966450e+00 -4.01754629e-01]
...
[-5.42806834e-02 -8.00926183e+00]
[-3.23018700e+02 -3.48773480e+00]
[-3.66934700e-01 -1.00238429e+01]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... -1 1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 15, 16, 17, 18, 22, 29, 48, 50, 51, 52, 53,
60, 67, 68, 71, 76, 80, 81, 82, 83, 84, 89,
98, 99, 100, 101, 112, 115, 117, 121, 142, 164, 167,
169, 170, 183, 198, 214, 217, 218, 219, 243, 244, 248,
249, 250, 251, 271, 281, 282, 283, 290, 291, 292, 297,
310, 314, 315, 316, 317, 319, 326, 327, 329, 331, 341,
343, 344, 348, 362, 366, 370, 379, 381, 386, 389, 399,
405, 448, 450, 451, 463, 469, 472, 473, 475, 481, 482,
512, 514, 521, 528, 533, 540, 568, 574, 575, 579, 584,
591, 592, 593, 594, 611, 614, 615, 620, 641, 643, 656,
658, 668, 669, 672, 673, 676, 677, 678, 681, 682, 683,
685, 689, 693, 694, 696, 697, 699, 700, 705, 706, 718,
719, 722, 723, 731, 732, 734, 735, 744, 746, 753, 754,
755, 759, 761, 766, 779, 780, 781, 792, 819, 877, 936,
973, 974, 984, 987, 996, 1011, 1013, 1014, 1017, 1134, 1138,
1139, 1150, 1152, 1178, 1203, 1222, 1224, 1235, 1236, 1238, 1246,
1247, 1248, 1251, 1252, 1253, 1254, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 183
朴素贝叶斯模型得到的AUC值： 0.79341164453524
对连续型特征向量第12轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-3.45433225e+01 -3.95261973e-01]
[-7.72970332e+00 -3.96609218e-01]
[-7.17073873e+00 -3.97619307e-01]
...
[-6.33273054e-02 -7.20479956e+00]
[-3.23973873e+02 -3.48773480e+00]
[-3.67047971e-01 -9.44476685e+00]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... -1 1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 16, 22, 48, 50, 51, 67, 68, 71, 76, 84, 89,
90, 98, 99, 101, 115, 117, 121, 183, 198, 214, 217,
218, 219, 248, 249, 250, 251, 281, 282, 297, 314, 317,
319, 327, 331, 344, 348, 366, 370, 379, 381, 386, 399,
448, 451, 469, 472, 475, 481, 482, 483, 521, 528, 540,
568, 575, 579, 611, 615, 635, 641, 656, 658, 668, 669,
672, 673, 676, 677, 678, 681, 682, 683, 685, 693, 694,
696, 697, 699, 700, 705, 706, 718, 722, 723, 735, 753,
754, 759, 779, 780, 781, 792, 819, 936, 973, 987, 996,
1013, 1014, 1138, 1139, 1150, 1152, 1222, 1235, 1236, 1246, 1247,
1248, 1251, 1252, 1253, 1254, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 116
朴素贝叶斯模型得到的AUC值： 0.8202976798482416
对连续型特征向量第13轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-7.99664350e+00 -3.96293421e-01]
[-8.74441380e+00 -3.95750150e-01]
[-8.29349484e+00 -3.96028397e-01]
...
[-9.75109933e-02 -6.09170281e+00]
[-3.21948938e+02 -3.48773480e+00]
[-3.67496311e-01 -8.43701125e+00]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... -1 1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 48, 67, 76, 84, 89, 98, 99, 114, 121, 164, 198,
217, 218, 219, 249, 251, 272, 281, 297, 314, 317, 318,
319, 327, 331, 366, 370, 381, 469, 481, 482, 521, 540,
543, 579, 611, 615, 641, 656, 658, 668, 672, 673, 676,
685, 693, 694, 697, 700, 718, 722, 754, 779, 780, 819,
936, 973, 1013, 1014, 1138, 1139, 1152, 1222, 1224, 1235, 1246,
1247, 1248, 1251, 1252, 1253, 1254, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 73
朴素贝叶斯模型得到的AUC值： 0.8375528965416605
对连续型特征向量第14轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-7.51512746e+00 -3.96931932e-01]
[-8.28653078e+00 -3.96033755e-01]
[-7.81051216e+00 -3.96504567e-01]
...
[-2.94710412e-01 -4.59750264e+00]
[-3.10470514e-02 -5.24515631e+03]
[-3.69661922e-01 -7.03507871e+00]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 16, 48, 67, 76, 84, 89, 98, 99, 101, 114, 164,
198, 217, 218, 219, 240, 249, 250, 251, 272, 281, 297,
314, 317, 318, 319, 327, 331, 366, 370, 381, 389, 399,
448, 469, 481, 482, 521, 540, 543, 575, 579, 611, 615,
641, 656, 658, 668, 672, 673, 676, 682, 685, 689, 693,
694, 697, 700, 718, 722, 730, 735, 753, 754, 759, 779,
780, 819, 936, 973, 996, 1013, 1014, 1138, 1139, 1150, 1152,
1222, 1224, 1235, 1246, 1247, 1248, 1251, 1252, 1253, 1254, 1255,
1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 89
朴素贝叶斯模型得到的AUC值： 0.8769881803589669
对连续型特征向量第15轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-9.18660739e+00 -3.95575661e-01]
[-9.95834971e+00 -3.95406950e-01]
[-9.48292807e+00 -3.95495206e-01]
...
[-8.22457454e-01 -3.69775924e+00]
[-3.10470514e-02 -5.24355906e+03]
[-3.76382347e-01 -5.83237524e+00]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 48, 67, 89, 90, 98, 99, 107, 108, 109, 110, 114,
187, 217, 218, 219, 240, 270, 281, 296, 297, 298, 299,
314, 319, 331, 332, 366, 370, 381, 469, 481, 483, 521,
540, 615, 641, 656, 657, 658, 674, 685, 693, 694, 695,
718, 779, 792, 906, 936, 973, 1138, 1152, 1222, 1234, 1246,
1247, 1248, 1251, 1252, 1253, 1254, 1255, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 63
朴素贝叶斯模型得到的AUC值： 0.8874215671968481
对连续型特征向量第16轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-9.36961609e+00 -3.95523193e-01]
[-1.01255205e+01 -3.95384630e-01]
[-9.65898080e+00 -3.95457552e-01]
...
[-9.19798076e-01 -3.62156563e+00]
[-3.10470514e-02 -5.24342767e+03]
[-3.77933543e-01 -5.68325029e+00]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 48, 67, 90, 98, 99, 107, 108, 109, 110, 114, 187,
217, 218, 219, 240, 270, 281, 296, 297, 298, 299, 314,
319, 331, 332, 366, 370, 381, 469, 481, 483, 521, 540,
615, 641, 656, 657, 658, 674, 685, 693, 694, 695, 718,
779, 906, 936, 973, 1138, 1152, 1222, 1234, 1246, 1247, 1248,
1251, 1252, 1253, 1254, 1255, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 61
朴素贝叶斯模型得到的AUC值： 0.8882241354151467
对连续型特征向量第17轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-6.21115635e+00 -4.01427765e-01]
[-7.01361655e+00 -3.98020934e-01]
[-6.53657978e+00 -3.99711223e-01]
...
[-1.09641932e-01 -5.88239867e+00]
[-3.10470514e-02 -5.24667409e+03]
[-3.67296871e-01 -8.76888504e+00]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 10, 11, 12, 13, 14, 15, 16, 17, 19, 22, 48,
50, 51, 52, 57, 58, 59, 60, 67, 71, 80, 81,
82, 83, 84, 85, 89, 90, 91, 92, 99, 107, 108,
109, 110, 115, 117, 121, 164, 165, 166, 168, 186, 187,
198, 199, 214, 217, 218, 219, 239, 240, 241, 248, 249,
250, 251, 259, 270, 271, 272, 281, 282, 283, 290, 291,
292, 296, 297, 298, 299, 309, 310, 311, 312, 314, 315,
317, 318, 319, 326, 327, 329, 330, 331, 332, 341, 343,
344, 362, 366, 367, 381, 386, 389, 390, 405, 431, 443,
448, 451, 469, 472, 473, 474, 475, 481, 483, 484, 485,
492, 493, 516, 528, 533, 543, 568, 574, 575, 579, 582,
584, 592, 593, 594, 611, 620, 635, 637, 656, 657, 658,
661, 663, 664, 668, 669, 672, 673, 674, 676, 677, 678,
681, 682, 683, 685, 689, 690, 693, 694, 695, 696, 697,
699, 700, 705, 706, 716, 717, 718, 730, 731, 734, 735,
736, 746, 753, 754, 755, 756, 759, 766, 779, 780, 781,
877, 878, 936, 973, 974, 987, 989, 996, 1013, 1014, 1138,
1139, 1150, 1178, 1196, 1203, 1222, 1224, 1234, 1237, 1238, 1246,
1248, 1251, 1252, 1253, 1254, 1255, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 205
朴素贝叶斯模型得到的AUC值： 0.784583394133956
对连续型特征向量第18轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-6.62027139e+00 -3.99353283e-01]
[-7.22778112e+00 -3.97488457e-01]
[-6.86807765e+00 -3.98453851e-01]
...
[-1.46969941e-01 -5.43326864e+00]
[-3.10470514e-02 -5.24617322e+03]
[-3.67626422e-01 -8.26793051e+00]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 10, 11, 12, 14, 16, 17, 22, 48, 50, 51, 52,
57, 58, 59, 60, 67, 71, 80, 81, 82, 84, 89,
90, 91, 92, 99, 107, 108, 109, 110, 117, 121, 164,
165, 166, 168, 187, 198, 199, 217, 218, 219, 239, 240,
248, 249, 250, 251, 270, 271, 272, 281, 282, 283, 290,
292, 296, 297, 298, 299, 309, 312, 314, 315, 317, 318,
319, 326, 327, 329, 330, 331, 332, 343, 344, 366, 367,
381, 386, 389, 390, 405, 443, 448, 451, 469, 472, 473,
474, 475, 481, 483, 484, 485, 492, 493, 528, 533, 543,
568, 574, 575, 576, 579, 582, 584, 611, 635, 637, 656,
657, 658, 668, 669, 672, 673, 674, 676, 677, 678, 681,
682, 683, 685, 689, 693, 694, 695, 696, 697, 699, 700,
705, 706, 716, 717, 718, 730, 731, 734, 735, 736, 746,
753, 754, 755, 756, 759, 766, 779, 780, 781, 878, 936,
973, 987, 996, 1013, 1014, 1138, 1139, 1150, 1178, 1196, 1203,
1222, 1224, 1234, 1248, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
测试集中预测为舞弊样本的个数有： 175
朴素贝叶斯模型得到的AUC值： 0.7507660878447395
对连续型特征向量第19轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-6.83797484e+00 -3.98551557e-01]
[-7.44059844e+00 -3.97061264e-01]
[-7.07693554e+00 -3.97851436e-01]
...
[-2.05543713e-01 -5.00069802e+00]
[-3.10470514e-02 -5.24574399e+03]
[-3.68114738e-01 -7.80819956e+00]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 10, 11, 12, 16, 17, 22, 48, 50, 57, 58, 59,
60, 71, 80, 82, 84, 89, 90, 91, 92, 99, 107,
108, 109, 110, 121, 164, 165, 166, 168, 187, 198, 217,
218, 219, 239, 240, 248, 249, 250, 251, 270, 271, 272,
281, 282, 296, 297, 298, 299, 312, 314, 315, 317, 318,
319, 326, 327, 329, 330, 331, 332, 366, 367, 381, 386,
389, 390, 443, 448, 451, 469, 472, 473, 474, 475, 481,
482, 483, 484, 485, 492, 493, 528, 533, 543, 568, 574,
575, 576, 579, 582, 584, 611, 635, 656, 657, 658, 672,
673, 674, 676, 677, 678, 681, 682, 683, 685, 689, 693,
694, 695, 696, 697, 699, 700, 705, 706, 716, 717, 718,
730, 731, 735, 736, 753, 754, 759, 766, 779, 780, 781,
936, 973, 987, 996, 1013, 1014, 1138, 1139, 1150, 1196, 1203,
1222, 1224, 1234, 1248, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
测试集中预测为舞弊样本的个数有： 153
朴素贝叶斯模型得到的AUC值： 0.7595943382460236
对连续型特征向量第20轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-7.17985500e+00 -3.97597890e-01]
[-7.73010576e+00 -3.96608676e-01]
[-7.39343752e+00 -3.97148235e-01]
...
[-2.62305780e-01 -4.72213685e+00]
[-3.10470514e-02 -5.24545834e+03]
[-3.68606139e-01 -7.49288319e+00]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 10, 12, 16, 22, 48, 57, 58, 84, 89, 90, 91,
92, 99, 107, 108, 109, 110, 121, 165, 166, 168, 187,
198, 217, 218, 219, 239, 240, 248, 249, 250, 251, 270,
271, 272, 281, 296, 297, 298, 299, 314, 317, 318, 319,
326, 327, 330, 366, 381, 386, 389, 390, 448, 451, 472,
473, 481, 482, 483, 484, 485, 492, 493, 533, 543, 568,
574, 575, 576, 579, 584, 611, 635, 656, 657, 658, 672,
673, 674, 676, 677, 681, 682, 683, 685, 689, 693, 694,
695, 697, 699, 700, 705, 716, 717, 718, 730, 731, 735,
736, 753, 754, 759, 779, 780, 781, 936, 973, 987, 996,
1013, 1014, 1138, 1139, 1150, 1196, 1203, 1222, 1224, 1234, 1248,
1251, 1252, 1253, 1254, 1255, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 127
朴素贝叶斯模型得到的AUC值： 0.7700277250839049
对连续型特征向量第21轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-3.61498758e+00 -4.81302265e-01]
[-4.14686332e+00 -4.44909537e-01]
[-3.85845847e+00 -4.62071928e-01]
...
[-6.84960061e-02 -6.93208405e+00]
[-3.10470514e-02 -2.19234751e+02]
[-3.72453619e-01 -6.35733839e+00]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 24, 29, 33, 34, 45, 48, 49, 50,
51, 52, 53, 55, 57, 58, 59, 60, 61, 65, 66,
67, 68, 69, 71, 76, 77, 80, 81, 82, 83, 84,
85, 86, 89, 90, 91, 92, 93, 98, 99, 100, 101,
103, 107, 108, 109, 110, 112, 113, 115, 117, 121, 122,
126, 127, 129, 130, 131, 138, 141, 149, 150, 154, 155,
156, 157, 164, 165, 166, 167, 168, 169, 170, 171, 176,
177, 178, 182, 183, 185, 186, 187, 188, 198, 199, 200,
201, 202, 203, 204, 213, 214, 217, 218, 219, 221, 222,
224, 225, 229, 230, 231, 232, 233, 236, 239, 240, 241,
242, 243, 244, 245, 246, 248, 249, 250, 251, 252, 256,
257, 258, 259, 260, 261, 263, 265, 267, 268, 270, 271,
272, 273, 274, 281, 282, 283, 284, 285, 290, 291, 292,
293, 296, 297, 298, 299, 300, 301, 302, 309, 310, 311,
312, 314, 315, 316, 317, 318, 319, 320, 326, 327, 328,
329, 330, 331, 332, 338, 339, 341, 342, 343, 344, 345,
346, 350, 351, 352, 353, 354, 355, 356, 361, 362, 366,
367, 368, 369, 370, 371, 376, 377, 378, 379, 381, 386,
387, 388, 389, 390, 391, 396, 397, 400, 401, 402, 405,
407, 409, 410, 419, 420, 421, 422, 423, 429, 430, 431,
432, 434, 435, 443, 448, 449, 450, 451, 452, 453, 454,
457, 458, 459, 460, 463, 468, 469, 470, 472, 473, 474,
475, 481, 482, 483, 484, 485, 492, 493, 495, 496, 497,
500, 503, 504, 512, 513, 514, 515, 516, 522, 526, 527,
528, 529, 532, 533, 534, 541, 543, 544, 545, 555, 568,
569, 570, 571, 572, 573, 574, 575, 576, 579, 580, 581,
582, 584, 591, 592, 593, 594, 596, 598, 599, 604, 609,
610, 611, 612, 613, 614, 615, 616, 617, 619, 620, 624,
633, 634, 635, 637, 640, 641, 642, 643, 651, 652, 653,
656, 657, 658, 660, 661, 662, 663, 664, 665, 666, 667,
668, 669, 670, 671, 672, 673, 674, 676, 677, 678, 679,
680, 681, 682, 683, 684, 685, 689, 690, 692, 693, 694,
695, 696, 697, 698, 699, 700, 701, 703, 704, 705, 706,
712, 713, 714, 715, 716, 717, 718, 719, 722, 723, 725,
727, 730, 731, 732, 733, 734, 735, 736, 742, 744, 745,
746, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759,
760, 761, 762, 766, 767, 768, 769, 770, 771, 775, 779,
780, 781, 782, 808, 809, 811, 812, 815, 819, 825, 827,
828, 831, 864, 877, 878, 893, 902, 905, 906, 917, 918,
936, 943, 951, 952, 973, 974, 976, 984, 986, 987, 988,
989, 990, 996, 1010, 1011, 1012, 1013, 1014, 1017, 1019, 1020,
1052, 1077, 1080, 1116, 1117, 1119, 1121, 1123, 1124, 1129, 1132,
1134, 1135, 1137, 1138, 1139, 1141, 1143, 1144, 1145, 1146, 1150,
1152, 1153, 1156, 1158, 1161, 1164, 1165, 1167, 1171, 1173, 1175,
1176, 1178, 1180, 1181, 1182, 1183, 1187, 1189, 1190, 1194, 1195,
1196, 1202, 1203, 1204, 1205, 1206, 1215, 1216, 1217, 1218, 1220,
1222, 1223, 1224, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241,
1242, 1243, 1244, 1246, 1247, 1248, 1249, 1251, 1252, 1253, 1254,
1255, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 541
朴素贝叶斯模型得到的AUC值： 0.7414635925871882
对连续型特征向量第22轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-4.23813220e+00 -4.40479415e-01]
[-4.35189131e+00 -4.35518042e-01]
[-4.28910675e+00 -4.38183295e-01]
...
[-9.15368855e-02 -6.21466016e+00]
[-3.10470514e-02 -2.18624949e+02]
[-3.78069727e-01 -5.67117107e+00]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
21, 22, 24, 29, 34, 45, 48, 49, 50, 51, 52,
53, 55, 57, 58, 59, 60, 61, 65, 67, 68, 71,
76, 80, 81, 82, 83, 84, 85, 86, 89, 90, 91,
92, 98, 99, 100, 101, 103, 107, 108, 109, 110, 112,
115, 117, 121, 122, 127, 131, 134, 150, 157, 164, 165,
166, 167, 168, 169, 170, 171, 173, 174, 175, 177, 178,
183, 185, 186, 187, 188, 198, 199, 200, 201, 202, 203,
204, 213, 214, 217, 218, 219, 222, 225, 229, 231, 232,
233, 239, 240, 241, 242, 243, 244, 248, 249, 250, 251,
252, 256, 257, 258, 259, 260, 263, 270, 271, 272, 273,
274, 281, 282, 283, 290, 291, 292, 296, 297, 298, 299,
300, 301, 309, 310, 311, 312, 314, 315, 316, 317, 318,
319, 320, 326, 327, 329, 330, 331, 332, 341, 342, 343,
344, 345, 346, 350, 351, 352, 356, 361, 362, 366, 367,
368, 369, 370, 371, 376, 377, 378, 379, 381, 386, 387,
388, 389, 390, 396, 400, 405, 406, 407, 409, 410, 419,
420, 421, 422, 423, 429, 430, 431, 432, 435, 443, 448,
450, 451, 452, 453, 454, 457, 458, 459, 460, 463, 468,
469, 470, 472, 473, 474, 475, 481, 482, 483, 484, 485,
492, 493, 495, 497, 500, 512, 513, 514, 516, 526, 527,
528, 529, 532, 533, 541, 543, 544, 555, 568, 569, 570,
571, 574, 575, 576, 579, 580, 581, 582, 584, 591, 592,
593, 594, 596, 597, 598, 599, 604, 608, 609, 610, 611,
612, 613, 615, 616, 620, 624, 632, 633, 634, 635, 637,
640, 641, 643, 651, 652, 653, 656, 657, 658, 660, 661,
662, 663, 664, 665, 667, 668, 669, 670, 671, 672, 673,
674, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685,
689, 690, 692, 693, 694, 695, 696, 697, 698, 699, 700,
701, 702, 703, 704, 705, 706, 714, 715, 716, 717, 718,
719, 725, 726, 727, 730, 731, 732, 733, 734, 735, 736,
737, 738, 742, 744, 745, 746, 747, 750, 751, 752, 753,
754, 755, 756, 757, 758, 759, 760, 761, 762, 764, 766,
767, 768, 769, 770, 771, 779, 780, 781, 782, 808, 809,
827, 828, 831, 877, 878, 893, 899, 917, 936, 951, 973,
974, 983, 984, 986, 987, 988, 989, 990, 996, 1010, 1011,
1012, 1013, 1014, 1017, 1019, 1044, 1052, 1053, 1054, 1080, 1087,
1116, 1119, 1120, 1124, 1129, 1134, 1138, 1139, 1143, 1145, 1147,
1150, 1152, 1153, 1155, 1158, 1161, 1164, 1165, 1171, 1173, 1175,
1176, 1178, 1181, 1182, 1184, 1187, 1189, 1190, 1194, 1195, 1196,
1202, 1203, 1205, 1206, 1215, 1216, 1217, 1218, 1219, 1222, 1223,
1224, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243,
1246, 1248, 1249, 1251, 1252, 1253, 1254, 1255, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 471
朴素贝叶斯模型得到的AUC值： 0.7236976506639428
对连续型特征向量第23轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-1.82456954 -1.07646741]
[-1.8511813 -1.05115322]
[-1.83536677 -1.0660378 ]
...
[-0.47928395 -4.12771781]
[-0.41949726 -4.62127084]
[-0.72268568 -2.38655493]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27,
28, 29, 30, 31, 32, 33, 34, 35, 41, 42, 43,
44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55,
57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 68,
69, 70, 71, 73, 74, 76, 77, 80, 81, 82, 83,
84, 89, 90, 91, 92, 93, 98, 99, 100, 101, 102,
103, 104, 107, 108, 109, 110, 111, 112, 113, 114, 115,
116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,
127, 128, 129, 130, 131, 132, 133, 134, 137, 138, 139,
140, 141, 142, 147, 148, 149, 150, 151, 152, 153, 154,
155, 156, 157, 164, 165, 166, 167, 168, 169, 170, 171,
174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,
185, 186, 187, 188, 189, 190, 191, 194, 196, 198, 199,
200, 201, 202, 203, 204, 205, 213, 214, 215, 217, 218,
219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229,
230, 231, 232, 233, 234, 235, 236, 237, 239, 240, 241,
242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252,
253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263,
264, 265, 267, 268, 270, 271, 272, 273, 274, 275, 281,
282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292,
293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 304,
309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319,
320, 326, 327, 328, 329, 330, 331, 332, 338, 339, 341,
342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352,
353, 354, 355, 356, 357, 359, 361, 362, 363, 366, 367,
368, 369, 370, 371, 372, 373, 374, 376, 377, 378, 379,
380, 381, 386, 387, 388, 389, 390, 391, 392, 396, 397,
398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408,
409, 410, 411, 412, 413, 414, 419, 420, 421, 422, 423,
424, 425, 429, 430, 431, 432, 433, 434, 435, 436, 437,
438, 443, 444, 445, 446, 448, 449, 450, 451, 452, 453,
454, 455, 456, 458, 459, 460, 461, 462, 463, 464, 465,
466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 481,
482, 483, 484, 485, 487, 488, 489, 490, 491, 492, 493,
494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504,
505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515,
516, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531,
532, 533, 534, 535, 541, 543, 544, 545, 550, 551, 552,
553, 554, 555, 560, 561, 562, 563, 568, 569, 570, 571,
572, 573, 574, 575, 576, 577, 579, 580, 581, 582, 583,
584, 585, 586, 587, 591, 592, 593, 594, 595, 596, 597,
598, 599, 604, 608, 609, 610, 611, 612, 613, 614, 615,
616, 617, 619, 620, 621, 622, 624, 625, 626, 632, 633,
634, 635, 640, 641, 642, 643, 651, 652, 653, 654, 656,
657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667,
668, 669, 670, 671, 672, 673, 674, 676, 677, 678, 679,
680, 681, 682, 683, 684, 685, 689, 690, 691, 692, 693,
694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704,
705, 706, 712, 713, 714, 715, 716, 717, 718, 719, 720,
721, 722, 723, 725, 726, 727, 729, 730, 731, 732, 733,
734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744,
745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755,
756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766,
767, 768, 769, 770, 771, 772, 773, 774, 775, 779, 780,
781, 782, 786, 787, 788, 789, 790, 791, 803, 804, 805,
806, 807, 808, 809, 810, 811, 812, 813, 815, 817, 819,
824, 825, 827, 828, 829, 831, 841, 842, 843, 844, 850,
851, 852, 853, 854, 860, 861, 862, 864, 865, 877, 878,
889, 890, 892, 893, 894, 899, 902, 905, 906, 907, 915,
917, 918, 919, 925, 930, 936, 942, 943, 945, 951, 952,
962, 970, 971, 973, 974, 975, 976, 983, 984, 985, 986,
987, 988, 989, 990, 994, 996, 997, 998, 1010, 1011, 1012,
1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1023, 1024, 1025,
1049, 1051, 1052, 1053, 1054, 1055, 1056, 1066, 1067, 1077, 1078,
1080, 1087, 1089, 1103, 1104, 1105, 1106, 1107, 1110, 1111, 1112,
1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123,
1124, 1125, 1127, 1129, 1132, 1134, 1135, 1136, 1137, 1138, 1139,
1140, 1141, 1142, 1143, 1144, 1145, 1146, 1148, 1150, 1152, 1153,
1155, 1156, 1158, 1161, 1162, 1164, 1165, 1167, 1171, 1172, 1173,
1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1186,
1187, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1198, 1199,
1200, 1202, 1203, 1204, 1205, 1206, 1208, 1209, 1214, 1215, 1216,
1217, 1218, 1219, 1220, 1222, 1223, 1224, 1227, 1228, 1229, 1231,
1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244,
1246, 1247, 1248, 1249, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
测试集中预测为舞弊样本的个数有： 835
朴素贝叶斯模型得到的AUC值： 0.6234860644973004
对连续型特征向量第24轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-1.80171371 -1.09929616]
[-1.80857468 -1.09233327]
[-1.82112388 -1.07984275]
...
[-0.60568094 -3.92921389]
[-0.56167423 -4.37503953]
[-0.91005896 -2.05027898]]
检查得到的对数概率shape中对应的分类标签： [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27,
28, 29, 30, 31, 32, 33, 34, 35, 41, 42, 43,
44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55,
56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,
67, 68, 69, 70, 71, 72, 76, 77, 80, 81, 82,
83, 84, 89, 90, 91, 92, 93, 98, 99, 100, 101,
102, 103, 104, 107, 108, 109, 110, 111, 112, 113, 114,
115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
126, 127, 128, 129, 130, 131, 132, 133, 134, 137, 138,
139, 140, 141, 142, 147, 148, 149, 150, 151, 152, 153,
154, 155, 156, 157, 164, 165, 166, 167, 168, 169, 170,
171, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183,
184, 185, 186, 187, 188, 189, 190, 191, 194, 195, 196,
197, 198, 199, 200, 201, 202, 203, 204, 205, 213, 214,
215, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226,
227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,
239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249,
250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,
261, 262, 263, 264, 265, 266, 267, 268, 270, 271, 272,
273, 274, 275, 281, 282, 283, 284, 285, 286, 287, 288,
289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,
300, 301, 302, 304, 309, 310, 311, 312, 313, 314, 315,
316, 317, 318, 319, 320, 326, 327, 328, 329, 330, 331,
332, 338, 339, 341, 342, 343, 344, 345, 346, 347, 348,
350, 351, 352, 353, 354, 355, 356, 357, 361, 362, 363,
366, 367, 368, 369, 370, 371, 372, 373, 374, 376, 377,
378, 379, 380, 381, 386, 387, 388, 389, 390, 391, 392,
396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406,
407, 408, 409, 410, 411, 412, 413, 414, 419, 420, 421,
422, 423, 424, 425, 429, 430, 431, 432, 433, 434, 435,
436, 437, 438, 443, 444, 445, 446, 447, 448, 449, 450,
451, 452, 453, 454, 455, 456, 458, 459, 460, 461, 462,
463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473,
474, 475, 481, 482, 483, 484, 485, 487, 488, 489, 490,
491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501,
502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512,
513, 514, 515, 516, 522, 523, 524, 525, 526, 527, 528,
529, 530, 531, 532, 533, 534, 535, 541, 543, 544, 545,
550, 551, 552, 553, 554, 555, 560, 561, 562, 563, 568,
569, 570, 571, 572, 573, 574, 575, 576, 577, 579, 580,
581, 582, 583, 584, 585, 586, 587, 591, 592, 593, 594,
595, 596, 597, 598, 599, 604, 608, 609, 610, 611, 612,
613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 624,
625, 626, 632, 633, 634, 635, 637, 639, 640, 641, 642,
643, 651, 652, 653, 654, 656, 657, 658, 659, 660, 661,
662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672,
673, 674, 676, 677, 678, 679, 680, 681, 682, 683, 684,
685, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698,
699, 700, 701, 702, 703, 704, 705, 706, 712, 713, 714,
715, 716, 717, 718, 719, 720, 721, 722, 723, 725, 726,
727, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738,
739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749,
750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760,
761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771,
772, 773, 774, 775, 779, 780, 781, 782, 786, 787, 788,
789, 790, 791, 803, 804, 805, 806, 807, 808, 809, 810,
811, 812, 813, 815, 817, 819, 824, 825, 827, 828, 829,
831, 841, 842, 843, 844, 850, 851, 852, 853, 854, 860,
861, 862, 864, 865, 877, 878, 889, 890, 892, 893, 894,
899, 902, 905, 906, 907, 915, 917, 918, 919, 925, 930,
936, 942, 943, 945, 951, 952, 962, 970, 971, 973, 974,
975, 976, 983, 984, 985, 986, 987, 988, 989, 990, 994,
996, 997, 998, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017,
1018, 1019, 1020, 1023, 1024, 1025, 1049, 1051, 1052, 1053, 1054,
1055, 1056, 1066, 1077, 1078, 1080, 1087, 1089, 1103, 1104, 1105,
1106, 1107, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118,
1119, 1120, 1121, 1122, 1123, 1124, 1125, 1127, 1129, 1132, 1134,
1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145,
1146, 1147, 1150, 1152, 1153, 1154, 1155, 1156, 1158, 1161, 1162,
1164, 1165, 1167, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178,
1179, 1180, 1181, 1182, 1183, 1184, 1186, 1187, 1189, 1190, 1191,
1192, 1193, 1194, 1195, 1196, 1198, 1199, 1200, 1202, 1203, 1204,
1205, 1206, 1208, 1209, 1213, 1214, 1215, 1216, 1217, 1218, 1219,
1220, 1221, 1222, 1223, 1224, 1227, 1228, 1229, 1234, 1235, 1236,
1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1246, 1247, 1248,
1249, 1251, 1252, 1253, 1254, 1255, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 843
朴素贝叶斯模型得到的AUC值： 0.6202757916241063
连续型特征的样本特征数目： [24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
朴素贝叶斯模型得到的AUC值分别是：
[0.5396906464322195, 0.5384867941047716, 0.5942652852765212, 0.5986794104771632, 0.6303808550999561, 0.6219538888078213, 0.6295782868816576, 0.6600758791770028, 0.6805413687436159, 0.7022107106376769, 0.79341164453524, 0.8202976798482416, 0.8375528965416605, 0.8769881803589669, 0.8874215671968481, 0.8882241354151467, 0.784583394133956, 0.7507660878447395, 0.7595943382460236, 0.7700277250839049, 0.7414635925871882, 0.7236976506639428, 0.6234860644973004, 0.6202757916241063]
对离散型特征向量第1轮迭代。
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 1, 2, 3, 4, 7, 8, 9, 10, 15, 16, 17,
18, 20, 22, 25, 26, 27, 29, 34, 42, 44, 45,
46, 47, 48, 50, 51, 52, 53, 57, 58, 59, 60,
63, 66, 67, 68, 69, 70, 76, 80, 81, 82, 83,
85, 86, 87, 88, 89, 90, 95, 98, 99, 100, 101,
102, 110, 117, 120, 121, 122, 127, 128, 129, 130, 131,
132, 134, 139, 140, 141, 142, 143, 144, 145, 146, 147,
148, 149, 151, 152, 154, 155, 156, 158, 164, 166, 167,
168, 169, 171, 174, 175, 176, 177, 179, 180, 181, 182,
184, 186, 188, 189, 193, 198, 201, 203, 205, 206, 207,
208, 210, 212, 217, 218, 219, 224, 227, 228, 229, 230,
231, 232, 233, 239, 240, 241, 242, 243, 244, 245, 246,
248, 249, 250, 251, 252, 253, 254, 255, 271, 273, 274,
275, 281, 282, 283, 284, 285, 286, 287, 289, 290, 291,
292, 293, 294, 295, 297, 302, 303, 305, 306, 309, 310,
311, 312, 313, 314, 315, 316, 327, 328, 329, 331, 332,
333, 334, 335, 336, 337, 338, 339, 341, 342, 343, 344,
345, 346, 347, 348, 349, 351, 352, 353, 354, 355, 357,
360, 361, 362, 363, 364, 365, 366, 368, 370, 376, 377,
378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388,
389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399,
400, 401, 402, 405, 406, 407, 408, 409, 415, 419, 420,
421, 422, 423, 424, 425, 426, 427, 428, 434, 435, 436,
437, 438, 439, 445, 446, 449, 450, 451, 452, 453, 457,
458, 459, 462, 469, 471, 472, 473, 475, 476, 477, 479,
481, 482, 483, 489, 490, 491, 492, 493, 495, 496, 497,
498, 499, 500, 501, 502, 505, 506, 507, 508, 509, 510,
511, 512, 514, 517, 518, 519, 520, 521, 522, 523, 524,
525, 527, 528, 530, 531, 532, 533, 535, 537, 538, 539,
540, 544, 545, 546, 553, 554, 555, 557, 560, 561, 562,
573, 574, 575, 576, 577, 579, 580, 581, 584, 586, 591,
592, 593, 594, 595, 597, 598, 599, 600, 601, 604, 605,
606, 609, 611, 616, 619, 620, 621, 622, 623, 625, 626,
633, 640, 643, 649, 651, 652, 653, 654, 658, 659, 662,
663, 668, 669, 670, 671, 672, 673, 676, 677, 679, 681,
682, 683, 684, 686, 687, 688, 690, 691, 693, 694, 696,
697, 698, 699, 700, 702, 703, 704, 705, 706, 707, 708,
709, 710, 711, 712, 713, 714, 715, 718, 719, 720, 721,
722, 723, 724, 725, 726, 727, 729, 730, 732, 733, 734,
735, 737, 739, 740, 742, 744, 746, 747, 748, 750, 751,
752, 753, 754, 755, 757, 758, 759, 760, 761, 762, 763,
764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774,
776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 788,
789, 791, 792, 793, 794, 796, 804, 805, 806, 807, 808,
809, 810, 811, 813, 814, 815, 817, 818, 819, 824, 825,
826, 827, 828, 829, 831, 836, 837, 838, 842, 843, 850,
851, 852, 853, 854, 860, 861, 862, 864, 865, 866, 867,
868, 872, 873, 875, 877, 889, 890, 891, 893, 894, 895,
897, 898, 899, 902, 903, 905, 907, 915, 916, 917, 918,
919, 925, 926, 927, 928, 929, 930, 938, 939, 941, 942,
943, 944, 952, 958, 959, 962, 963, 970, 971, 974, 975,
983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993,
995, 997, 1010, 1011, 1012, 1013, 1014, 1016, 1017, 1018, 1019,
1023, 1024, 1025, 1043, 1044, 1045, 1049, 1050, 1051, 1052, 1053,
1054, 1055, 1066, 1068, 1069, 1070, 1075, 1076, 1077, 1080, 1081,
1104, 1105, 1106, 1107, 1110, 1111, 1112, 1113, 1115, 1116, 1118,
1119, 1120, 1122, 1123, 1124, 1125, 1134, 1136, 1137, 1138, 1139,
1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1150, 1152, 1153,
1155, 1157, 1158, 1161, 1162, 1163, 1165, 1168, 1171, 1172, 1173,
1174, 1177, 1178, 1180, 1181, 1182, 1184, 1185, 1186, 1189, 1190,
1191, 1193, 1201, 1202, 1203, 1204, 1205, 1206, 1208, 1209, 1215,
1216, 1217, 1218, 1219, 1221, 1222, 1223, 1224, 1225, 1226, 1228,
1229, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244,
1245, 1246, 1247, 1248, 1251, 1252, 1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 701
朴素贝叶斯模型得到的AUC值： 0.5396906464322195
对离散型特征向量第2轮迭代。
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 1, 2, 3, 4, 7, 8, 9, 15, 16, 17, 18,
25, 26, 27, 29, 34, 42, 45, 46, 47, 48, 50,
51, 52, 53, 59, 60, 63, 66, 68, 69, 70, 76,
80, 81, 82, 83, 85, 86, 87, 88, 89, 95, 96,
97, 98, 99, 100, 101, 102, 117, 120, 121, 127, 128,
129, 130, 131, 132, 139, 140, 141, 142, 143, 144, 145,
146, 148, 149, 151, 152, 154, 155, 156, 158, 164, 167,
168, 169, 171, 174, 175, 176, 177, 179, 180, 181, 186,
188, 189, 192, 193, 198, 201, 203, 205, 206, 207, 208,
210, 211, 212, 217, 218, 219, 224, 227, 228, 229, 231,
232, 233, 239, 241, 243, 244, 245, 248, 250, 251, 252,
253, 254, 255, 271, 273, 274, 275, 277, 278, 279, 280,
281, 282, 283, 284, 285, 286, 287, 289, 290, 291, 292,
293, 294, 295, 297, 302, 303, 305, 306, 309, 310, 311,
312, 313, 314, 315, 316, 321, 327, 328, 329, 332, 333,
334, 335, 336, 337, 338, 339, 341, 342, 343, 344, 345,
346, 347, 348, 349, 351, 352, 353, 354, 355, 357, 360,
361, 362, 363, 364, 365, 366, 368, 370, 376, 377, 378,
379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389,
390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400,
401, 402, 405, 406, 407, 408, 415, 418, 419, 420, 421,
422, 423, 424, 425, 426, 427, 428, 434, 435, 436, 437,
438, 439, 441, 445, 446, 449, 450, 451, 452, 453, 457,
458, 459, 462, 469, 471, 472, 473, 475, 476, 477, 479,
482, 483, 489, 490, 491, 492, 493, 495, 496, 497, 498,
499, 500, 501, 502, 505, 506, 507, 508, 509, 510, 511,
512, 514, 517, 518, 519, 520, 521, 522, 523, 524, 525,
527, 528, 530, 531, 532, 533, 535, 537, 538, 539, 540,
545, 546, 548, 553, 554, 555, 557, 558, 559, 560, 561,
562, 567, 574, 575, 576, 577, 579, 580, 581, 584, 591,
592, 593, 594, 595, 597, 598, 600, 601, 602, 603, 604,
605, 606, 607, 609, 611, 619, 620, 621, 622, 623, 626,
640, 643, 644, 646, 647, 648, 649, 651, 652, 653, 654,
658, 659, 662, 668, 669, 670, 671, 672, 673, 676, 677,
679, 681, 682, 683, 684, 686, 687, 688, 690, 691, 693,
694, 696, 697, 698, 700, 703, 704, 705, 706, 707, 708,
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1247, 1248, 1251, 1252, 1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 765
朴素贝叶斯模型得到的AUC值： 0.5140084634466657
离散型特征的样本特征数目： [2, 1]
朴素贝叶斯模型得到的AUC值分别是：
[0.5396906464322195, 0.5140084634466657]
In [884]:
# 根据上一步，我们选择18个连续特征，个离散特征训练朴素贝叶斯模型。
auc_bayes_continuous = np.array(auc_bayes_iter_contin)
best_param_op_idx_bayes_continuous = np.max(np.where(auc_bayes_continuous==np.max(auc_bayes_continuous))[0])
print("最佳超参的索引best_param_op_idx_bayes_continuous是：", best_param_op_idx_bayes_continuous)
X_train_continuous_op = X_train_continuous[:,best_param_op_idx_bayes_continuous:]
X_test_continuous_op = X_test_continuous[:,best_param_op_idx_bayes_continuous:]
print("X_train_continuous_op.shape is", X_train_continuous_op.shape)
auc_bayes_discrete = np.array(auc_bayes_iter_discrete)
best_param_op_idx_bayes_discrete = np.max(np.where(auc_bayes_discrete==np.max(auc_bayes_discrete))[0])
print("最佳超参的索引best_param_op_idx_bayes_discrete是：", best_param_op_idx_bayes_discrete)
X_train_discrete_op = X_train_discrete[:, best_param_op_idx_bayes_discrete:] # 离散型特征向量只有2个，为简化计算，直接采用上一步得到的X_train_discrete。
X_test_discrete_op = X_test_discrete[:, best_param_op_idx_bayes_discrete:] # 与X_train_discrete_op同理。
print("X_train_continuous_op.shape is:\n",X_train_continuous_op.shape)
print("X_train_discrete_op.shape is:\n",X_train_discrete_op.shape)
# 生成一个长度为训练集样本容量大小的list，将负例样本的权重设为大于1的某个整数。这个sample_weight在连续型样本和离散型样本上通用。
# 权重为array-like，1 for unweighted samples。也可以None，表示全体样本无权重
sample_weight_value = 120
sample_weight_train = [sample_weight_value if train_set.loc[i, "label"]==-1 else 1
for i in range(train_continuous_set.shape[0])]
# 对连续型特征向量进行高斯朴素贝叶斯训练
clf_gauss_op = GaussianNB() # 初始化高斯朴素贝叶斯模型实例
clf_gauss_op.partial_fit(X_train_continuous_op, y_train, classes=[1,-1], sample_weight=sample_weight_train)
continuous_pred_prob_op = clf_gauss_op.predict_log_proba(X_test_continuous_op)
print("检查连续型特征向量的输出概率的shape:",continuous_pred_prob_op.shape)
# 对离散型特征变量进行多项式朴素贝叶斯训练
clf_multinomial_op = MultinomialNB() # 默认使用拉普拉斯平滑，alpha=1
clf_multinomial_op.partial_fit(X_train_discrete_op, y_train, classes=[1,-1], sample_weight=sample_weight_train)
discrete_pred_prob_op = clf_multinomial_op.predict_log_proba(X_test_discrete_op)
print("检查离散型特征向量输出概率的shape:",discrete_pred_prob_op.shape)
# 然后将上一步得到的离散特征向量的输出概率和连续特征向量的输出概率相加，作为最终全部向量空间的输出概率。根据该输出概率判断样本属于哪个
# 分类。
y_pred_prob_op = continuous_pred_prob_op + discrete_pred_prob_op
print("最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:\n", y_pred_prob_op)
print("检查得到的对数概率shape中对应的分类标签：", clf_gauss_op.classes_, clf_multinomial_op.classes_)
y_pred_bayes_op = np.argmax(y_pred_prob_op, axis=1) # 沿着列方向比较大小，取最大值所在的index。
y_pred_bayes_op[y_pred_bayes_op==0] = -1
print("朴素贝叶斯预测的结果是：\n", y_pred_bayes_op)
print("朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：\n", np.where(y_pred_bayes_op==-1))
print("测试集中真实舞弊样本的index是：\n", y_test[y_test==-1])
print("测试集中预测为舞弊样本的个数有：", np.where(y_pred_bayes_op==-1)[0].size)
# 计算训练模型clf_op的AUC值，评估模型质量。
auc_value_op_bayes = auc(y_test, y_pred_bayes_op)
print("朴素贝叶斯模型得到的AUC值：", auc_value_op_bayes)
# 打印朴素贝叶斯的分类效果报告
print("用最优朴素贝叶斯模型，得到的分类结果评估报告如下：\n", classification_report(y_test, y_pred_bayes_op))
最佳超参的索引best_param_op_idx_bayes_continuous是： 15
X_train_continuous_op.shape is (1257, 9)
最佳超参的索引best_param_op_idx_bayes_discrete是： 0
X_train_continuous_op.shape is:
(1257, 9)
X_train_discrete_op.shape is:
(1257, 2)
检查连续型特征向量的输出概率的shape: (1257, 2)
检查离散型特征向量输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
[[-9.06813013e+00 -4.58918975e-01]
[-9.82400685e+00 -4.58752706e-01]
[-9.35748172e+00 -4.58840210e-01]
...
[-8.10043294e-01 -3.87641788e+00]
[-2.59416512e-02 -5.24378710e+03]
[-3.23559845e-01 -5.99350139e+00]]
检查得到的对数概率shape中对应的分类标签： [-1 1] [-1 1]
朴素贝叶斯预测的结果是：
[ 1 1 1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
(array([ 48, 67, 89, 90, 91, 98, 99, 107, 108, 109, 110,
114, 187, 217, 218, 219, 240, 270, 281, 296, 297, 298,
299, 314, 317, 318, 319, 331, 332, 366, 370, 381, 469,
481, 483, 521, 540, 615, 641, 656, 657, 658, 673, 674,
685, 693, 694, 695, 700, 718, 736, 779, 792, 906, 936,
973, 1138, 1152, 1222, 1224, 1234, 1246, 1247, 1248, 1251, 1252,
1253, 1254, 1255, 1256], dtype=int64),)
测试集中真实舞弊样本的index是：
[-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1]
测试集中预测为舞弊样本的个数有： 70
朴素贝叶斯模型得到的AUC值： 0.884612578432803
用最优朴素贝叶斯模型，得到的分类结果评估报告如下：
precision recall f1-score support
-1 0.13 0.82 0.22 11
1 1.00 0.95 0.97 1246
avg / total 0.99 0.95 0.97 1257
In [892]:
# 现在，我们比较了SVC，Logistic Regression和Naive Bayes三种分类器的性能。我们比较下这三个分类器的期望错误分类成本的大小。EC，即Excepted
# MisClassification Cost，EC=P_error1*P_prior_positive*Cost_error1 + P_error2*P_prior_negative*Cost_error2，具体解释就是：
# P_error1指第一类错误的概率，当样本为正例时，分类器判为负例的概率。P_prior_positive是正例的先验概率，Cost_error1指发生第一类错误的
# 成本代价；同理，P_error2指第二类错误的概率，当样本为负例时，分类器判为正例的概率，P_prior_negative指负例的先验概率，Cost_error2指发生
# 第二类错误时的成本代价。在我们的分类器结果评估报告中，我们用各分类的1-recall作为P_error1和P_error2。非舞弊样本先验概率我们假设为0.98，
# 舞弊样本先验概率假设为0.02，Cost_error1和Cost_error2分别为1和10。
P_prior_positive = 0.98
Cost_error1 = 1
P_prior_negative = 0.02
Cost_error2 = 10
P_error1_svc = 1/1246
P_error2_svc = (11-5)/11
P_error1_lg = (159-10)/1246
P_error2_lg = (11-10)/11
P_error1_bayes = (70-9)/1246
P_error2_bayes = (11-9)/11
EC_svc = P_error1_svc*P_prior_positive*Cost_error1 + P_error2_svc*P_prior_negative*Cost_error2
EC_lg = P_error1_lg*P_prior_positive*Cost_error1 + P_error2_lg*P_prior_negative*Cost_error2
EC_bayes = P_error1_bayes*P_prior_positive*Cost_error1 + P_error2_bayes*P_prior_negative*Cost_error2
print("EC_svc is:",EC_svc,"\nEC_lg is:",EC_lg,"\nEC_bayes",EC_bayes)
print("我们选择EC值最小的模型，因此选NB。综合分析模型分类准确率和计算成本，SVC的运算成本较高。逻辑回归比朴素贝叶斯少一些数据的处理，\
综合来看应该是最佳选择。考虑到微调逻辑回归决策函数的概率阈值，还能够将分类指标提升。逻辑回归和朴素贝叶斯模型差别不大。")
EC_svc is: 0.10987742594484166 EC_lg is: 0.13537282941777323 EC_bayes 0.08434116445352399 我们选择EC值最小的模型，因此选SVC。但是SVC的运算成本较高。综合选择，逻辑回归是最佳选择。但其实逻辑回归和朴素贝叶斯模型差别很微弱， 而3个模型其实差别并不大。